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ChristBERT, a domain-specific German RoBERTa model, outperforms existing models on German medical NLP benchmarks.

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ChristBERT models trained on a 13.5GB German medical corpus via continued pre-training, from-scratch training, and vocabulary adaptation outperform prior general and medical German models on four of five NER and classification benchmarks.

T0 review reviewed 2026-06-28 challenge →

load-bearing objection ChristBERT gives clear gains on five German medical benchmarks via task-dependent adaptation and releases the models, but the broad SOTA claim rests on a narrow task set. the 2 major comments →

arxiv 2606.03250 v1 pith:EF74PE27 submitted 2026-06-02 cs.CL

The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP

Henry He , Johann Frei , Raphael Schmitt This is my paper
classification cs.CL
keywords German medical NLPdomain-specific pre-trainingChristBERTclinical language modelsRoBERTanamed entity recognitiontext classification
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

German clinical text requires language models adapted to medical terminology and style rather than general language. The authors train ChristBERT on a 13.5GB corpus of publications, clinical notes, and health web content using different adaptation strategies. They evaluate on named entity recognition and text classification tasks in the medical domain. The specialized models set a new state of the art by beating general and prior medical German models on four of five tasks. The choice between training from scratch and continued pre-training depends on how specialized the target text is.

Core claim

ChristBERT establishes a new state of the art for German clinical language modeling by outperforming existing general-purpose and medical German language models on four of five benchmarks, with the optimal pre-training strategy being task-dependent: training from scratch for highly specialized clinical texts and continued pre-training for more common medical texts.

What carries the argument

Comparison of continued pre-training, training from scratch, and domain-specific vocabulary adaptation on a 13.5GB German medical corpus for RoBERTa-based models.

Load-bearing premise

The five evaluated tasks are representative of German clinical language modeling performance in general.

What would settle it

Demonstrating that ChristBERT underperforms on a new, broader set of German medical NLP tasks such as clinical question answering or summarization.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Training from scratch is particularly effective for highly specialized clinical texts.
  • Continued pre-training performs well on more commonly written medical texts.
  • Domain-specific models outperform general-purpose and existing medical German models on most evaluated tasks.
  • Public release of the models enables further research in German medical NLP.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The task-dependent nature of optimal strategies implies that model developers should test multiple approaches for new domains.
  • Improved performance on clinical NER and classification could support better AI applications in German healthcare documentation.
  • Results highlight the value of large domain-specific corpora even for languages with limited resources.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces ChristBERT, a family of German RoBERTa-based models pre-trained on a 13.5 GB corpus spanning scientific publications, clinical texts, health-related web content, and translated resources. It compares three domain-adaptation strategies (continued pre-training, training from scratch, and domain-specific vocabulary adaptation) and evaluates the resulting models on three medical named-entity recognition tasks and two text classification tasks. The central claim is that ChristBERT outperforms existing general-purpose and medical German models on four of five benchmarks, establishes a new state of the art for German clinical language modeling, and that the optimal adaptation strategy is task-dependent.

Significance. If the reported outperformance holds under scrutiny, the work supplies publicly released models and concrete empirical guidance on when continued pre-training versus from-scratch training is preferable for German medical text. The explicit comparison of adaptation strategies is a useful contribution to the limited literature on non-English clinical language modeling.

major comments (2)
  1. [Abstract] Abstract: the assertion that ChristBERT 'establishes a new state of the art for German clinical language modeling' is not supported by the reported experiments. The evaluation is confined to three NER and two classification tasks; no argument is supplied that these tasks are representative of the distribution of clinical NLP problems, nor are results shown for other task families (relation extraction, coreference, summarization, QA, or temporal reasoning).
  2. [Evaluation section and results tables] Evaluation (presumed §4–5) and results tables: the manuscript provides no statistical significance tests, confidence intervals, or details on data splits and baseline implementations. Without these, the claim of consistent outperformance on four of five benchmarks cannot be verified as robust.
minor comments (2)
  1. [Abstract] The abstract and introduction could more explicitly list the five benchmark datasets and their sources.
  2. [Methods / Corpus description] Corpus composition statistics (token counts per source type) would help readers assess domain coverage.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which highlight important aspects of our evaluation and claims. We address each major point below and commit to revisions that strengthen the manuscript without altering its core contributions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion that ChristBERT 'establishes a new state of the art for German clinical language modeling' is not supported by the reported experiments. The evaluation is confined to three NER and two classification tasks; no argument is supplied that these tasks are representative of the distribution of clinical NLP problems, nor are results shown for other task families (relation extraction, coreference, summarization, QA, or temporal reasoning).

    Authors: We agree that the phrasing in the abstract overstates the scope of our results. Our evaluation is limited to the five reported tasks, which are standard benchmarks in the German medical NLP literature but do not cover the full range of clinical NLP problems. We will revise the abstract to state that ChristBERT establishes a new state of the art on the evaluated benchmarks rather than claiming it for German clinical language modeling in general. This change requires no additional experiments. revision: yes

  2. Referee: [Evaluation section and results tables] Evaluation (presumed §4–5) and results tables: the manuscript provides no statistical significance tests, confidence intervals, or details on data splits and baseline implementations. Without these, the claim of consistent outperformance on four of five benchmarks cannot be verified as robust.

    Authors: The referee correctly identifies a gap in the current manuscript. We will add statistical significance testing (e.g., paired t-tests or McNemar’s test with p-values), bootstrap confidence intervals for the reported F1 and accuracy scores, explicit descriptions of train/validation/test splits for each dataset, and full details on how the baseline models were implemented and fine-tuned. These additions will be placed in the Evaluation section and results tables to allow verification of the robustness of the outperformance claims. revision: yes

Circularity Check

0 steps flagged

No circularity in derivation chain

full rationale

The paper reports an empirical study: models are trained on a described corpus using standard RoBERTa procedures, then evaluated on five fixed external benchmarks (3 NER + 2 classification). No equations, parameter-fitting steps, or self-referential definitions appear in the provided text. The SOTA claim rests solely on measured F1/accuracy deltas against prior models; these comparisons are independent of the training process itself and do not reduce to any fitted input or self-citation chain. The representativeness concern raised by the skeptic is a question of external validity, not circularity of the reported results.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Paper relies on standard transformer pre-training assumptions and empirical benchmarking; no novel mathematical derivations or invented physical entities.

free parameters (1)
  • pre-training hyperparameters (learning rate, batch size, epochs)
    Standard values chosen during training but not detailed in abstract; central claims depend on these choices.
axioms (1)
  • domain assumption RoBERTa architecture remains effective when adapted to domain-specific German medical text
    Paper builds all models on RoBERTa without re-deriving or questioning its base suitability.

reviewed 2026-06-28 · how reviews work

0 comments
Cite this review

Pith. "Pith review of The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP." pith.science (2026). https://pith.science/paper/EF74PE27

@misc{pith2026260603250,
  author       = {Pith},
  title        = {Pith review of: The Word and the Way: Strategies for Domain-Specific BERT Pre-Training in German Medical NLP},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EF74PE27}},
  note         = {Machine review of arXiv:2606.03250}
}
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read the original abstract

Digital healthcare generates vast amounts of clinical text that can support AI-assisted applications, yet German biomedical language models remain limited by older architectures or restricted training data. We present ChristBERT (Clinical- and Healthcare-Related Issues and Subjects Tuned BERT), a family of domain-specific German RoBERTa-based language models trained on a 13.5GB corpus of scientific publications, clinical texts, health-related web content, and translated clinical resources. To investigate the impact of domain adaptation strategies in German clinical NLP, we compare continued pre-training, training from scratch, and domain-specific vocabulary adaptation. The resulting models are evaluated on three medical named entity recognition tasks and two text classification tasks. ChristBERT consistently outperforms existing general-purpose and medical German language models on four of five benchmarks and establishes a new state of the art for German clinical language modeling. Our results show that the optimal adaptation strategy is task-dependent: in our evaluation, training from scratch is particularly effective for highly specialized clinical texts, whereas continued pre-training performs well on more commonly written medical texts. All models are publicly released to support future research and applications in German medical NLP.

discussion (0)

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Reference graph

Works this paper leans on

103 extracted references · 3 canonical work pages

  1. [1]

    Clinical information extraction applications: a literature review

    Wang Y, Wang L, Rastegar-Mojarad M, Moon S, Shen F, Afzal N, et al. Clinical information extraction applications: a literature review. Journal of biomedical informatics. 2018;77:34–49

    2018

  2. [2]

    The Stockholm EPR Corpus-characteristics and some initial findings

    Dalianis H, Hassel M, Velupillai S. The Stockholm EPR Corpus-characteristics and some initial findings. Proceedings of ISHIMR. 2009;p. 243–249

    2009

  3. [3]

    Natural language process- ing and the representation of clinical data

    Sager N, Lyman M, Bucknall C, Nhan N, Tick LJ. Natural language process- ing and the representation of clinical data. Journal of the American Medical Informatics Association. 1994;1(2):142–160

    1994

  4. [4]

    TEXTINFO: a tool for automatic determination of patient clinical profiles using text analysis

    Borst F, Lyman M, Nhan N, Tick L, Sager N, Scherrer J. TEXTINFO: a tool for automatic determination of patient clinical profiles using text analysis. In: Proceedings of the Annual Symposium on Computer Application in Medical Care; 1991. p. 63

    1991

  5. [5]

    Architectural requirements for a multipurpose natural language processor in the clinical environment

    Friedman C, Johnson SB, Forman B, Starren J. Architectural requirements for a multipurpose natural language processor in the clinical environment. In: Proceedings of the Annual Symposium on Computer Application in Medical Care; 1995. p. 347

    1995

  6. [6]

    Natural language processing for smart healthcare

    Zhou B, Yang G, Shi Z, Ma S. Natural language processing for smart healthcare. IEEE Reviews in Biomedical Engineering. 2022;17:4–18

    2022

  7. [7]

    Attention is all you need

    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17. Red Hook, NY, USA: Curran Associates Inc.; 2017. p. 6000–6010

    2017

  8. [8]

    BERT: Pre-training of Deep Bidi- rectional Transformers for Language Understanding

    Devlin J, Chang MW, Lee K, Toutanova K. BERT: Pre-training of Deep Bidi- rectional Transformers for Language Understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers); 2019. p. 4171–4186

    2019

  9. [9]

    Roberta: A robustly optimized bert pretraining approach

    Liu Y. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:190711692. 2019

    2019

  10. [10]

    When BERT Started Traveling: TourBERT—A Natural Language Processing Model for the Travel Industry

    Arefeva V, Egger R. When BERT Started Traveling: TourBERT—A Natural Language Processing Model for the Travel Industry. Digital. 2022;2(4):546–559

    2022

  11. [11]

    Challenges and opportunities beyond structured data in analysis of electronic health records

    Tayefi M, Ngo P, Chomutare T, Dalianis H, Salvi E, Budrionis A, et al. Challenges and opportunities beyond structured data in analysis of electronic health records. Wiley Interdisciplinary Reviews: Computational Statistics. 2021;13(6):e1549. 32

    2021

  12. [12]

    SciBERT: A Pretrained Language Model for Scientific Text

    Beltagy I, Lo K, Cohan A. SciBERT: A Pretrained Language Model for Scientific Text. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP); 2019. p. 3615–3620

    2019

  13. [13]

    Clinicalbert: Modeling clinical notes and predicting hospital readmission

    Huang K, Altosaar J, Ranganath R. Clinicalbert: Modeling clinical notes and predicting hospital readmission. arXiv preprint arXiv:190405342. 2019

    2019

  14. [14]

    Transfer Learning in Biomedical Natural Language Pro- cessing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets

    Peng Y, Yan S, Lu Z. Transfer Learning in Biomedical Natural Language Pro- cessing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasets. In: Proceedings of the 18th BioNLP Workshop and Shared Task; 2019. p. 58–65

    2019

  15. [15]

    BioBERT: a pre- trained biomedical language representation model for biomedical text mining

    Lee J, Yoon W, Kim S, Kim D, Kim S, So CH, et al. BioBERT: a pre- trained biomedical language representation model for biomedical text mining. Bioinformatics. 2020;36(4):1234–1240

    2020

  16. [16]

    Critical assessment of transformer-based AI models for German clinical notes

    Lentzen M, Madan S, Lage-Rupprecht V, K¨ uhnel L, Fluck J, Jacobs M, et al. Critical assessment of transformer-based AI models for German clinical notes. JAMIA open. 2022;5(4):ooac087

    2022

  17. [17]

    Bressem KK, Papaioannou JM, Grundmann P, Borchert F, Adams LC, Liu L, et al. Medbert. de: A comprehensive german bert model for the medical domain. Expert Systems with Applications. 2024;237:121598

    2024

  18. [18]

    GeistBERT: Breathing Life into German NLP

    Scheible-Schmitt R, Frei J. GeistBERT: Breathing Life into German NLP. In: Das SB, Mishra P, Singh A, Muhammad SH, Ekbal A, Das UK, editors. Proceedings of the Workshop on Beyond English: Natural Language Process- ing for all Languages in an Era of Large Language Models. Varna, Bulgaria: INCOMA Ltd., Shoumen, BULGARIA; 2025. p. 42–50. Available from: http...

    2025

  19. [19]

    Understanding Back-Translation at Scale

    Edunov S, Ott M, Auli M, Grangier D. Understanding Back-Translation at Scale. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing; 2018. p. 489–500

    2018

  20. [20]

    Facebook FAIR’s WMT19 News Translation Task Submission

    Ng N, Yee K, Baevski A, Ott M, Auli M, Edunov S. Facebook FAIR’s WMT19 News Translation Task Submission. In: Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1). Association for Computational Linguistics; 2019

    2019

  21. [21]

    No language left behind: Scaling human-centered machine translation

    Costa-juss` a MR, Cross J, C ¸ elebi O, Elbayad M, Heafield K, Heffernan K, et al. No language left behind: Scaling human-centered machine translation. arXiv preprint arXiv:220704672. 2022

    2022

  22. [22]

    An overview of MetaMap: historical perspective and recent advances

    Aronson AR, Lang FM. An overview of MetaMap: historical perspective and recent advances. Journal of the American Medical Informatics Association. 2010;17(3):229–236. 33

    2010

  23. [23]

    Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

    Savova GK, Masanz JJ, Ogren PV, Zheng J, Sohn S, Kipper-Schuler KC, et al. Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications. Journal of the American Medical Informatics Association. 2010;17(5):507–513

    2010

  24. [24]

    A broad-coverage natural language processing system

    Friedman C. A broad-coverage natural language processing system. In: Proceedings of the AMIA Symposium; 2000. p. 270

    2000

  25. [25]

    CLAMP–a toolkit for efficiently building customized clinical natural language processing pipelines

    Soysal E, Wang J, Jiang M, Wu Y, Pakhomov S, Liu H, et al. CLAMP–a toolkit for efficiently building customized clinical natural language processing pipelines. Journal of the American Medical Informatics Association. 2018;25(3):331–336

    2018

  26. [26]

    2010 i2b2/VA challenge on con- cepts, assertions, and relations in clinical text

    Uzuner ¨O, South BR, Shen S, DuVall SL. 2010 i2b2/VA challenge on con- cepts, assertions, and relations in clinical text. Journal of the American Medical Informatics Association. 2011;18(5):552–556

    2010

  27. [27]

    2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records

    Henry S, Buchan K, Filannino M, Stubbs A, Uzuner O. 2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records. Journal of the American Medical Informatics Association. 2020;27(1):3–12

    2018

  28. [28]

    Cohort selection for clinical trials: n2c2 2018 shared task track 1

    Stubbs A, Filannino M, Soysal E, Henry S, Uzuner ¨O. Cohort selection for clinical trials: n2c2 2018 shared task track 1. Journal of the American Medical Informatics Association. 2019;26(11):1163–1171

    2018

  29. [29]

    Crestani F, Braschler M, Savoy J, Rauber A, M¨ uller H, Losada DE, et al. Exper- imental IR Meets Multilinguality, Multimodality, and Interaction: 10th Interna- tional Conference of the CLEF Association, CLEF 2019, Lugano, Switzerland, September 9–12, 2019, Proceedings. vol. 11696. Springer Nature; 2019

    2019

  30. [30]

    Distributed Representa- tions of Words and Phrases and their Compositionality

    Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed Representa- tions of Words and Phrases and their Compositionality. In: Advances in Neural Information Processing Systems. vol. 26. Curran Associates, Inc.; 2013

    2013

  31. [31]

    Dissecting Contextual Word Embeddings: Architecture and Representation

    Peters ME, Neumann M, Zettlemoyer L, Yih Wt. Dissecting Contextual Word Embeddings: Architecture and Representation. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing; 2018. p. 1499–1509

    2018

  32. [32]

    Bag of Tricks for Efficient Text Classification

    Joulin A, Grave ´E, Bojanowski P, Mikolov T. Bag of Tricks for Efficient Text Classification. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers; 2017. p. 427–431

    2017

  33. [33]

    CamemBERT: a Tasty French Language Model

    Martin L, Muller B, Ortiz Su´ arez PJ, Dupont Y, Romary L, de la Clergerie ´E, et al. CamemBERT: a Tasty French Language Model. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics; 2020. p. 7203–7219. Available from: 34 https://www.aclweb.org/anthology/2020.acl-main.645

    2020

  34. [34]

    Unsupervised Cross-lingual Representation Learning at Scale

    Conneau A, Khandelwal K, Goyal N, Chaudhary V, Wenzek G, Guzm´ an F, et al. Unsupervised Cross-lingual Representation Learning at Scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

  35. [35]

    Got- tBERT: a pure German Language Model

    Scheible R, Frei J, Thomczyk F, He H, Tippmann P, Knaus J, et al. Got- tBERT: a pure German Language Model. In: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. Miami, Florida, USA: Association for Computational Linguistics; 2024. p. 21237–21250

    2024

  36. [36]

    German’s Next Language Model

    Chan B, Schweter S, M¨ oller T. German’s Next Language Model. In: Proceedings of the 28th International Conference on Computational Linguistics; 2020. p. 6788–6796

    2020

  37. [37]

    Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures

    Su´ arez PJO, Sagot B, Romary L. Asynchronous pipeline for processing huge corpora on medium to low resource infrastructures. In: 7th Workshop on the Challenges in the Management of Large Corpora (CMLC-7). Leibniz-Institut f¨ ur Deutsche Sprache; 2019

    2019

  38. [38]

    BERTje: A Dutch BERT model

    De Vries W, van Cranenburgh A, Bisazza A, Caselli T, van Noord G, Nissim M. BERTje: A Dutch BERT model. arXiv preprint arXiv:191209582. 2019

    2019

  39. [39]

    PubMed 2.0

    White J. PubMed 2.0. Medical reference services quarterly. 2020;39(4):382–387

    2020

  40. [40]

    Accessed on 2025-01-

    National Library of Medicine.: PMC Open Access Subset. Accessed on 2025-01-

    2025

  41. [41]

    Available from: https://pmc.ncbi.nlm.nih.gov/tools/openftlist/

  42. [42]

    Med-BERT: A pretraining framework for medical records named entity recognition

    Liu N, Hu Q, Xu H, Xu X, Chen M. Med-BERT: A pretraining framework for medical records named entity recognition. IEEE Transactions on Industrial Informatics. 2021;18(8):5600–5608

    2021

  43. [43]

    MIMIC-IV, a freely accessible electronic health record dataset

    Johnson AE, Bulgarelli L, Shen L, Gayles A, Shammout A, Horng S, et al. MIMIC-IV, a freely accessible electronic health record dataset. Scientific data. 2023;10(1):1

    2023

  44. [44]

    PhysioNet

    Johnson A, Pollard T, Horng S, Celi LA, Mark R.: MIMIC-IV-Note: Deidenti- fied free-text clinical notes. PhysioNet. Available from: https://physionet.org/ content/mimic-iv-note/

  45. [45]

    Named entity recognition in Spanish biomedical literature: Short review and BERT model

    Akhtyamova L. Named entity recognition in Spanish biomedical literature: Short review and BERT model. In: 2020 26th Conference of Open Innovations Association (FRUCT). IEEE; 2020. p. 1–7

    2020

  46. [46]

    BioBERTpt: a Portuguese neural language model for 35 clinical named entity recognition

    Rubel Schneider ET, Andrioli de Souza JV, Knafou J, Oliveira LE, Gumiel YB, de Oliveira LF, et al. BioBERTpt: a Portuguese neural language model for 35 clinical named entity recognition. In: Proceedings of the 3rd Clinical Natural Language Processing Workshop. 19 November 2020; 2020

    2020

  47. [47]

    Contextual- ized French language models for biomedical named entity recognition

    Copara J, Knafou J, Naderi N, Moro C, Ruch P, Teodoro D. Contextual- ized French language models for biomedical named entity recognition. In: 6e Conf´ erence Conjointe Journ´ ees D’´ etudes Sur La Parole (Jep, 33e Edition), Traitement Automatique Des Langues Naturelles (Taln, 27e Edition), Rencontre Des Etudiants Chercheurs En Informatique Pour Le Traitem...

    2020

  48. [48]

    How to improve infor- mation extraction from German medical records

    Starlinger J, Kittner M, Blankenstein O, Leser U. How to improve infor- mation extraction from German medical records. It-Information Technology. 2017;59(4):171–179

    2017

  49. [49]

    Sharing models and tools for pro- cessing German clinical texts

    Hellrich J, Matthies F, Faessler E, Hahn U. Sharing models and tools for pro- cessing German clinical texts. In: Digital Healthcare Empowering Europeans. IOS Press; 2015. p. 734–738

    2015

  50. [50]

    Sharing Copies of Synthetic Clinical Corpora without Physical Distribution — A Case Study to Get Around IPRs and Pri- vacy Constraints Featuring the German JSYNCC Corpus

    Christina Lohr SB, Hahn U. Sharing Copies of Synthetic Clinical Corpora without Physical Distribution — A Case Study to Get Around IPRs and Pri- vacy Constraints Featuring the German JSYNCC Corpus. In: Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). European Language Resources Association (ELRA); 2018

    2018

  51. [51]

    GGPONC 2.0-the German clinical guideline corpus for oncology: Curation work- flow, annotation policy, baseline NER taggers

    Borchert F, Lohr C, Modersohn L, Witt J, Langer T, Follmann M, et al. GGPONC 2.0-the German clinical guideline corpus for oncology: Curation work- flow, annotation policy, baseline NER taggers. In: Proceedings of the Thirteenth Language Resources and Evaluation Conference; 2022. p. 3650–3660

    2022

  52. [52]

    Annotation and initial evaluation of a large annotated German oncological corpus

    Kittner M, Lamping M, Rieke DT, G¨ otze J, Bajwa B, Jelas I, et al. Annotation and initial evaluation of a large annotated German oncological corpus. JAMIA open. 2021;4(2):ooab025

    2021

  53. [53]

    Annotated dataset creation through large language models for non-english medical NLP

    Frei J, Kramer F. Annotated dataset creation through large language models for non-english medical NLP. Journal of Biomedical Informatics. 2023;145:104478

    2023

  54. [54]

    Open Agrar Repository

    Neves M, Butzke D, D¨ orendahl A, Leich N, Grune B, Sch¨ onfelder G.: Non- technical Summaries (NTS) of Animal Experiments Indexed with ICD-10 Codes (Version 1.0). Open Agrar Repository. Available from: https://www.openagrar. de/receive/openagrar mods 00046540

  55. [55]

    Open Agrar Repository

    Neves M, Butzke D, D¨ orendahl A, Leich N, Grune B, Sch¨ onfelder G.: Test set of Non-technical Summaries (NTS) of Animal Experiments Indexed with ICD-10 Codes (Version 1.0). Open Agrar Repository. Available from: https: //www.openagrar.de/receive/openagrar mods 00049062. 36

  56. [56]

    The ICD-10 classification of mental and behavioural dis- orders: clinical descriptions and diagnostic guidelines

    Organization WH. The ICD-10 classification of mental and behavioural dis- orders: clinical descriptions and diagnostic guidelines. vol. 1. World Health Organization; 1992

    1992

  57. [57]

    Classifying German Animal Experiment Summaries with Multi-lingual BERT at CLEF eHealth 2019 Task 1.; 2019

    S¨ anger M, Weber L, Kittner M, Leser U. Classifying German Animal Experiment Summaries with Multi-lingual BERT at CLEF eHealth 2019 Task 1.; 2019

    2019

  58. [58]

    On the Impact of Cross-Domain Data on German Language Models

    Dada A, Chen A, Peng C, Smith KE, Idrissi-Yaghir A, Seibold CM, et al. On the Impact of Cross-Domain Data on German Language Models. In: The 2023 Conference on Empirical Methods in Natural Language Processing; 2023

    2023

  59. [59]

    Peer review: a flawed process at the heart of science and journals

    Smith R. Peer review: a flawed process at the heart of science and journals. Journal of the royal society of medicine. 2006;99(4):178–182

    2006

  60. [60]

    Data structures for statistical computing in python

    McKinney W, et al. Data structures for statistical computing in python. In: Proceedings of the 9th Python in Science Conference. vol. 445. Austin, TX; 2010. p. 51–56

    2010

  61. [61]

    Accessed on 2024-11-10

    Springer Nature.: Springer Nature Developer Portal – APIs for Research Papers. Accessed on 2024-11-10. Available from: https://dev.springernature.com/

    2024

  62. [62]

    Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset XML Dataset

    Achakulvisut T, Acuna D, Kording K. Pubmed Parser: A Python Parser for PubMed Open-Access XML Subset and MEDLINE XML Dataset XML Dataset. Journal of Open Source Software. 2020;5(46):1979. https://doi.org/10.21105/ joss.01979

    2020

  63. [63]

    Accessed on 2025-02-15

    winstxnhdw.: nllb-api. Accessed on 2025-02-15. Available from: https://github. com/winstxnhdw/nllb-api

    2025

  64. [64]

    Accessed on 2024-11-10

    Freie Universit¨ at Berlin.: Refubium – Home. Accessed on 2024-11-10. Available from: https://refubium.fu-berlin.de/

    2024

  65. [65]

    Accessed on 2024-11-

    Wikipedia contributors.: Seiten exportieren – Wikipedia. Accessed on 2024-11-

    2024

  66. [66]

    Available from: https://de.wikipedia.org/wiki/Spezial:Exportieren

  67. [67]

    Analysis and design of selenium webdriver automation testing framework

    Gojare S, Joshi R, Gaigaware D. Analysis and design of selenium webdriver automation testing framework. Procedia Computer Science. 2015;50:341–346

    2015

  68. [68]

    Original-date: 2012-05-20T18:45:54Z

    Kurtovic B.: earwig/mwparserfromhell. Original-date: 2012-05-20T18:45:54Z. Available from: https://github.com/earwig/mwparserfromhell

    2012

  69. [69]

    PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals

    Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, Mark RG, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. circulation. 2000;101(23):e215–e220

    2000

  70. [70]

    The llama 3 herd of models

    Dubey A, Jauhri A, Pandey A, Kadian A, Al-Dahle A, Letman A, et al. The llama 3 herd of models. arXiv preprint arXiv:240721783. 2024;. 37

    2024

  71. [71]

    Building a Scalable Health Information Crawler: Leveraging Apache Nutch for the tala-med Search Engine

    Deng N, Boeker M, Scheible R. Building a Scalable Health Information Crawler: Leveraging Apache Nutch for the tala-med Search Engine. In: Proceedings of MIE 2025. Glasgow, Scotland: IOPress; 2025

    2025

  72. [72]

    Nutch: A flexible and scalable open- source web search engine

    Khare R, Cutting D, Sitaker K, Rifkin A. Nutch: A flexible and scalable open- source web search engine. Oregon State University. 2004;1:32–32

    2004

  73. [73]

    Specht L, Scheible R, Boeker M, Farin-Glattacker E, Kampel N, Schm¨ olz M, et al. Evaluating the Acceptance and Usability of an Independent, Noncommer- cial Search Engine for Medical Information: Cross-Sectional Questionnaire Study and User Behavior Tracking Analysis. JMIR Hum Factors. 2025 Jan;12:e56941. https://doi.org/10.2196/56941

    work page doi:10.2196/56941 2025

  74. [74]

    Crawling the german health web: Exploratory study and graph analysis

    Zowalla R, Wetter T, Pfeifer D. Crawling the german health web: Exploratory study and graph analysis. Journal of medical Internet research. 2020;22(7):e17853

    2020

  75. [75]

    Accessed on 2025-02-05

    tblock.: 10kGNAD. Accessed on 2025-02-05. Available from: https://tblock. github.io/10kGNAD/

    2025

  76. [76]

    One million posts: A data set of german online discussions

    Schabus D, Skowron M, Trapp M. One million posts: A data set of german online discussions. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval; 2017. p. 1241–1244

    2017

  77. [77]

    Accessed on 2025-02-05

    Tkachenko M, Malyuk M, Holmanyuk A, Liubimov N.: Label Studio: Data Labeling Software. Accessed on 2025-02-05. Available from: https://github. com/heartexlabs/label-studio

    2025

  78. [78]

    Accessed on 2025-02-05

    jfilter.: clean-text. Accessed on 2025-02-05. Available from: https://github.com/ jfilter/clean-text

    2025

  79. [79]

    Accessed on 2025-07-31

    Schmitt R.: ChristBERT/sciGNAD tcls·Hugging Face. Accessed on 2025-07-31. Available from: https://huggingface.co/ChristBERT/sciGNAD tcls

    2025

  80. [80]

    Accessed on 2025-07-

    He H.: ChristBERT/sciGNAD·Datasets at Hugging Face. Accessed on 2025-07-

    2025

Showing first 80 references.

This paper was first reviewed by grok-4.3 on June 28, 2026.