Paper Citation Record · LEDGER

Can human clinical rationales improve the performance and explainability of clinical text classification models?

As of 7 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2507.21302.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.21302 v1

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T13:00:04.312988Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

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Source: cited_works

Reference resolution

39 of 39 outbound references displayed

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External citation measurements

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Outbound references

Observation 5ab510bd-95c9-4d9c-be81-5c00656768b5 · outbound

This paper cites Ai-driven clinical decision support systems: an ongoing pursuit of potential.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Ai-driven clinical decision support systems: an ongoing pursuit of potential

Reference 1

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Observation 2c7e05a9-89e5-4b07-83b7-9ab22879528f · outbound

This paper cites Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing

Reference 2

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Observation eaf5b8b6-7bab-4655-878b-642b6e14d7a5 · outbound

This paper cites Evaluating and Characterizing Human Rationales.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Evaluating and Characterizing Human Rationales

Reference 3

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Observation 2d5e0670-5170-431e-8652-154fdc2233b0 · outbound

This paper cites Knife: Distilling meta- reasoning knowledge with free-text rationales.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Knife: Distilling meta- reasoning knowledge with free-text rationales

Reference 4

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Observation d1a9cd3a-ca80-4190-8839-9599945a3e77 · outbound

This paper cites Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning

Reference 5

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Observation 4b79bb45-4ad2-424e-93e6-87aaad72a61b · outbound

This paper cites Large language models are clinical reasoners: Reasoning- aware diagnosis framework with prompt-generated rationales.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Large language models are clinical reasoners: Reasoning- aware diagnosis framework with prompt-generated rationales

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:00:04.891897Z

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Observation 4382dbe0-193b-4b63-9eda-e6a900e31810 · outbound

This paper cites Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Diagnostic reasoning prompts reveal the potential for large language model interpretability in medicine

Reference 7

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Observation e7218c9e-aaf5-4d0b-a409-29067a79cf92 · outbound

This paper cites annotator rationales.

Can human clinical rationales improve the performance and explainability of clinical text classification models? annotator rationales

Reference 8

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Observation 3aa08243-54e6-4dba-ad86-50ad37b2944a · outbound

This paper cites Rationale-augmented convolutional neural networks for text clas- sification.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Rationale-augmented convolutional neural networks for text clas- sification

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T13:00:04.850321Z

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Observation 87872b0b-4df5-4303-9950-0f4e9bac858d · outbound

This paper cites Deriving Machine Attention from Human Rationales.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Deriving Machine Attention from Human Rationales

Reference 10

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Source-reported events for the cited work

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Observation f7b6eee7-9ae4-4345-8474-de28a295db67 · outbound

This paper cites Do Human Rationales Improve Machine Explanations?.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Do Human Rationales Improve Machine Explanations?

Reference 11

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Observation e7145913-d836-4cff-b8f2-a92bb6f896e0 · outbound

This paper cites Fine-grained Sentiment Analysis with Faithful Attention.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Fine-grained Sentiment Analysis with Faithful Attention

Reference 12

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Observation 9102abd2-de98-40af-b48f-d35371b8f5f9 · outbound

This paper cites Marta: Leveraging human rationales for explainable text classification.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Marta: Leveraging human rationales for explainable text classification

Reference 13

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Observation f746415d-f886-47ae-828a-3bb9fec51aff · outbound

This paper cites Human-like explanation for text classification with limited attention supervision.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Human-like explanation for text classification with limited attention supervision

Reference 14

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Observation 1d4f5c23-f120-4d64-b688-56d03319fb9e · outbound

This paper cites Learning with rationales for document classification.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Learning with rationales for document classification

Reference 15

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Observation 0868a2b8-c7c4-4e30-9ac4-77def29b771e · outbound

This paper cites Rationale production to support clinical decision-making.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Rationale production to support clinical decision-making

Reference 16

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Observation 53f2f937-d927-452b-8d21-070af4829ff4 · outbound

This paper cites Explain and predict, and then predict again.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Explain and predict, and then predict again

Reference 17

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Observation afafb91c-1eda-4efa-85c5-0a205022edaa · outbound

This paper cites Evaluating explanations: How much do explanations from the teacher aid students? Transactions of the Association for Computational Linguistics, 10:359–375, 2022.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Evaluating explanations: How much do explanations from the teacher aid students? Transactions of the Association for Computational Linguistics, 10:359–375, 2022

Reference 18

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Observation 7b8f7b97-e4b5-4cf1-a4a9-bb60a400cf11 · outbound

This paper cites Rationalization for explainable nlp: a survey.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Rationalization for explainable nlp: a survey

Reference 19

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Observation 9ce6dd69-dc05-4d34-8766-e9d4ad9902fc · outbound

This paper cites Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Exploring the Trade-off Between Model Performance and Explanation Plausibility of Text Classifiers Using Human Rationales

Reference 20

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Observation 702ed879-b2c9-44b5-84fb-4fc98fa3e07f · outbound

This paper cites Contrastive Explanations for Model Interpretability.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Contrastive Explanations for Model Interpretability

Reference 21

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This paper cites https://seer.cancer.gov/data-software/.

Can human clinical rationales improve the performance and explainability of clinical text classification models? https://seer.cancer.gov/data-software/

Reference 22

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Observation da7f39e7-a5c7-4edf-bc4e-d1c50dedba95 · outbound

This paper cites ERASER: A Benchmark to Evaluate Rationalized NLP Models.

Can human clinical rationales improve the performance and explainability of clinical text classification models? ERASER: A Benchmark to Evaluate Rationalized NLP Models

Reference 23

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This paper cites Machine learning and deep learning tools for the automated capture of cancer surveillance data.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Machine learning and deep learning tools for the automated capture of cancer surveillance data

Reference 24

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Observation 886f9713-b0d0-48af-8488-34896d57ea0d · outbound

This paper cites SEER*DMS User Manual: Chapter 14 - Annotation Tasks, 2020.

Can human clinical rationales improve the performance and explainability of clinical text classification models? SEER*DMS User Manual: Chapter 14 - Annotation Tasks, 2020

Reference 25

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This paper cites A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports.

Can human clinical rationales improve the performance and explainability of clinical text classification models? A comparative study of large language model-based zero-shot inference and task-specific supervised classification of breast cancer pathology reports

Reference 26

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This paper cites Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Clinical-Longformer and Clinical-BigBird: Transformers for long clinical sequences

Reference 27

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This paper cites Attention mechanisms in clinical text classification: A comparative evaluation.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Attention mechanisms in clinical text classification: A comparative evaluation

Reference 28

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This paper cites Deformable phrase level attention: A flexible approach for improving ai based medical coding.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Deformable phrase level attention: A flexible approach for improving ai based medical coding

Reference 29

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This paper cites Text classification algorithms: A survey.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Text classification algorithms: A survey

Reference 30

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This paper cites Limitations of transformers on clinical text classification.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Limitations of transformers on clinical text classification

Reference 31

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This paper cites Deep learning–based text classification: a comprehensive review.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Deep learning–based text classification: a comprehensive review

Reference 32

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This paper cites Automatic classification of cancer pathology reports: a systematic review.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Automatic classification of cancer pathology reports: a systematic review

Reference 33

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Observation 87dc8c1e-0a1b-48a1-8a70-454c76f5348c · outbound

This paper cites Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

Reference 34

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This paper cites Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control

Reference 35

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This paper cites Is attention explanation? an introduction to the debate.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Is attention explanation? an introduction to the debate

Reference 36

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This paper cites Competency Problems: On Finding and Removing Artifacts in Language Data.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Competency Problems: On Finding and Removing Artifacts in Language Data

Reference 37

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This paper cites Towards faithful model explanation in nlp: A survey.

Can human clinical rationales improve the performance and explainability of clinical text classification models? Towards faithful model explanation in nlp: A survey

Reference 38

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This paper cites What to learn, and how: Toward effective learning from rationales.

Can human clinical rationales improve the performance and explainability of clinical text classification models? What to learn, and how: Toward effective learning from rationales

Reference 39

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Pith citing papers

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