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REVIEW 4 major objections 5 minor 1 cited by

Rephrasing Electronic Health Records for Pretraining Clinical Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Rephrasing real clinical notes with small LLMs yields synthetic pretraining corpora that outperform from-scratch synthesis.

desk verdict Worth reading for the fixed-budget rephrasing comparison, but the augmentation claim is likely confounded by a doubled token count. read the letter →

arxiv 2411.18940 v1 pith:SQD5P2VZ submitted 2024-11-28 cs.CL

Jinghui Liu , Anthony Nguyen This is my paper · ORCID
classification cs.CL
keywords clinicallanguagemodelssyntheticpretrainingdatarephrasingelectronichealthrecordsprivacy-preservingNLPLLMparaphraseMIMIC-III
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Clinical language models need large amounts of clinical text, but privacy rules make real electronic health record (EHR) notes hard to collect and share. This paper proposes generating synthetic pretraining corpora by asking small instruction-tuned LLMs (under 10 billion parameters) to paraphrase real discharge summaries, rather than inventing clinical notes from scratch. Across causal language modeling perplexity and downstream fine-tuning tasks, models pretrained on the rephrased corpora outperformed models trained on synthetic notes generated without referencing real text, and augmenting real notes with rephrased corpora improved performance even with only 20 million tokens. The result suggests that privacy-preserving pretraining can be built on paraphrase rather than generation, and that the method can support institution-level or larger-scale clinical language model development.

What carries the argument

The central machinery is the rephrasing pipeline: real clinical notes are split into chunks of about 300 tokens, each chunk is sent to a small instruction-tuned LLM under one of three prompts, and the outputs form a synthetic corpus used to pretrain or augment clinical language models. The role of the LLM is constrained to paraphrase, preserving the structure and content of real notes while changing surface form, which contrasts with from-scratch synthesis that relies on the LLM's internal knowledge to generate clinical details. Prompt choice matters: Prompt 3, which explicitly asks the LLM to explain medical terms, can hurt perplexity but helps on downstream natural language inference. Combining synthetic corpora from different LLMs or prompts with real notes provides stable gains.

What would settle it

Extract clinical concepts, medications, doses, negations, and temporal relations from the original and rephrased versions of the same notes and compare their distributions; if they diverge systematically, or if a blinded clinician review finds fabricated facts in the rephrased text, then the method's benefit is distributional mimicry rather than content preservation. A direct test would corrupt a set of original notes with a wrong medication, rephrase them, and check whether the pretrained model learns the corrupted fact.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that rephrasing, not fresh synthesis, is the better route to synthetic clinical pretraining text. Using discharge summaries from MIMIC-III as source material, the authors prompt four instruction-tuned LLMs (Llama-3.1 8B, Mistral-0.3 7B, Qwen-2 7B, and Gemma-2 9B) to rewrite chunks of roughly 300 tokens under three prompts: a general Wikipedia-style paraphrase, a medically worded paraphrase, and a medical paraphrase that additionally asks for explanations of medical terms. A 110M-parameter Llama pretrained on each 20M-token synthetic corpus reaches lower perplexity on held-out MIMIC-IV discharge summaries and radiology reports than a model pretrained on Asclepius-style synthetic notes generated from scratch, in most cases by a large margin. Combining real notes with rephrased corpora lowers perplexity further, and BERT models initialized from BioBERT and pretrained on real-plus-rephrased text beat the ClinicalBERT baseline on MedNLI and i2b2 2010/2012 while using roughly 20M tokens versus the full MIMIC-III corpus.

Load-bearing premise

The central assumption is that LLM rephrasing preserves the clinical content and meaning of the original notes closely enough that the synthetic text is both safe and useful for pretraining; the paper explicitly flags that it does not measure subtle meaning shifts or hallucinations.

Editorial extensions

If this is right

  • Institutions can pretrain or fine-tune clinical language models on rephrased versions of their own notes, potentially sharing the models without sharing raw patient text.
  • A small token budget of 20M rephrased tokens is enough to improve perplexity and downstream task performance, making the approach feasible for institution-level use and scalable to larger corpora.
  • Combining synthetic corpora from different LLMs or prompts yields additional gains, suggesting a recipe for scaling generation by diversity rather than by a single generator.
  • Masked language models pretrained with real-plus-rephrased text outperform the widely used ClinicalBERT on MedNLI and i2b2 benchmarks while using far fewer tokens.
  • The mixed results for medically adapted and explanation-seeking prompts indicate that prompt design is a controllable factor in synthetic clinical pretraining data quality.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If rephrasing preserves clinical meaning, the privacy argument is strengthened, but the paper does not measure re-identification risk; a testable extension is adversarial membership inference on the rephrased corpus to see whether it leaks information about the original notes.
  • The prompt-3 result hints at a tradeoff between content-preserving paraphrase and knowledge injection: adding explanations helps tasks like NLI that benefit from enriched context, while hurting language-model perplexity; measuring factual consistency across prompts would clarify this tradeoff.
  • The method may transfer to other note types such as radiology reports, progress notes, or discharge summaries in other languages, especially those with high boilerplate, though the paper only generates from MIMIC-III discharge summaries and evaluates on discharge summaries and radiology reports.
  • Because the paper does not include a qualitative clinical review of rephrased notes, a clinician-annotated comparison of original versus rephrased notes for factual errors would be a direct next step.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The manuscript proposes generating synthetic clinical pretraining corpora by prompting four small instruction-tuned LLMs (<10B) to rephrase chunks of MIMIC-III discharge summaries. The authors evaluate the approach in two settings: (i) causal language modeling with a 110M Llama model, measuring perplexity on MIMIC-IV discharge summaries and radiology reports under a temporal train/test split, and (ii) masked language modeling from BioBERT, fine-tuned on MedNLI, i2b2 2010, and i2b2 2012. The reported results indicate that rephrased corpora yield lower perplexity than the from-scratch Asclepius synthetic corpora, that augmenting real notes with synthetic corpora improves perplexity relative to real notes alone, and that synthetically augmented BERT models outperform ClinicalBERT on the downstream tasks.

Significance. If the main effects survive appropriate control conditions, this work would offer a practical, data-efficient recipe for institutional clinical language model pretraining, a timely contribution given the privacy constraints on sharing electronic health records. The study has notable methodological strengths: the synthetic-only comparison holds the token budget fixed at 20M across LLMs and prompts; the same source chunks are fed to all LLMs, controlling for input variation; and the test sets incorporate a temporal shift (MIMIC-IV notes from 2014 onward) and a different note type (radiology reports), making the perplexity evaluation more realistic than a random split. The rephrasing idea is well motivated by prior work on web-data rephrasing and is extended here with medical prompts, multiple LLMs, and both decoder- and encoder-based pretraining. However, the augmentation claim is currently confounded by doubled training tokens, and the absence of uncertainty quantification weakens the granular comparisons across prompts and LLMs.

major comments (4)
  1. [Section 3.2 / Figure 2; Section 4.1 / Table 1] Please see above.
  2. [Section 3 / Figures 1-3 / Table 1] See above.
  3. [Section 3.1 / Figure 1] See above.
  4. [Section 5] See above.
minor comments (5)
  1. [Section 2.3, 3.2, 4.1, Figure 1 caption] There are several typos: "syntatically diverse" (Section 2.3), "clincal notes" (Section 3.2), "identifical" (Section 4.1), and "Ascplepius" in the Figure 1 caption. These should be corrected.
  2. [Appendix A] The sentence "we did not show [the original chunk] here due to data agreement by MIMIC-III" is vague; it should be clarified whether the original chunk is omitted because of the MIMIC-III data use agreement and whether a redacted version could be included for comparison.
  3. [Appendix A / Section 3.2] The statement that "Llama seems more likely to use a boilerplate" is informal and unquantified; if this is intended to explain Llama's underperformance, it should be supported by an analysis of repeated patterns across generated chunks.
  4. [Section 4.1] Fine-tuning hyperparameters for the BERT models are not reported beyond the pretraining configuration (learning rate, epochs, batch size). These details are needed for reproducibility of the MedNLI and i2b2 results.
  5. [Section 3.1] The choice to initialize the decoder from a TinyStories-pretrained 110M model is not justified; the manuscript should state whether this initialization could affect the relative comparisons across synthetic corpora, even if all conditions share the same base model.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the claims rest on external benchmarks and no fitted parameter is renamed as a prediction.

full rationale

The paper's derivation chain is empirical rather than definitional: LLM rephrasing of MIMIC-III discharge summaries is used to pretrain models that are then evaluated on MIMIC-IV notes with a temporal shift, so the test set is not the same data that generated the training corpora. The rephrasing recipe, including the 300-token chunking rule and Prompt 1, is imported from Maini et al. (2024), an external source, and the comparison against Asclepius is an external baseline; no uniqueness theorem or self-citation is invoked to force the method. The main concerns raised by the manuscript are validity threats, not circularity: (i) because MIMIC-III and MIMIC-IV come from the same institution, some of the perplexity advantage over from-scratch synthesis may reflect distributional proximity rather than intrinsic corpus quality; (ii) the augmentation result in Figure 2 may compare a 20M-token original-only baseline with runs that see 20M original plus 20M synthetic tokens, so the gain could be driven by data volume unless a like-for-like token budget control is reported; and (iii) Section 5 itself flags that semantic preservation and hallucination are unmeasured ('we need to understand whether LLMs' rephrasing causes subtle shifts in clinical meaning and the extent of possible hallucinations'). These are important limitations of the empirical claims, but none of them makes the prediction equivalent to the input by construction, and the paper's self-citations are not load-bearing.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new mathematical or physical entities. Its contribution is empirical; the assumptions are domain-level premises about text fidelity, distributional validity of the temporal shift, proxy validity of small-model perplexity, and privacy preservation. None of these are established inside the paper.

free parameters (1)
  • Token budget for corpora
    All pretraining corpora are capped at 20M tokens, a choice made for efficient experimentation. The augmentation claim depends on this budget.
assumptions (4)
  • domain assumption LLM rephrasing of clinical notes preserves enough clinical content and meaning to serve as useful pretraining data.
    The entire method assumes rephrasing retains the clinical information needed for downstream tasks; Section 5 explicitly lists 'subtle shifts in clinical meaning and the extent of possible hallucinations' as unmeasured.
  • domain assumption MIMIC-IV notes from 2014+ constitute a valid out-of-distribution test for models trained on MIMIC-III-derived synthetic data.
    The temporal shift (Section 3.1) is the only guard against distributional overlap; if MIMIC-III and IV discharge summaries are near-identical in style, the perplexity advantage over Asclepius is partly trivial.
  • domain assumption Perplexity of a 110M TinyStories-initialized model reflects pretraining corpus quality.
    The claim of reduced perplexity (Figure 1) is measured on a tiny model; transfer of this ranking to larger models is assumed.
  • domain assumption Synthetic rephrased corpora can be shared or used without exposing patient information.
    The privacy motivation in Section 1 presumes rephrased text is not identifiable, but no PHI-leakage or re-identification evaluation is performed.

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Cite this review

Pith. "Pith review of Rephrasing Electronic Health Records for Pretraining Clinical Language Models." pith.science (2026). https://pith.science/paper/SQD5P2VZ

@misc{pith2026241118940,
  author       = {Pith},
  title        = {Pith review of: Rephrasing Electronic Health Records for Pretraining Clinical Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SQD5P2VZ}},
  note         = {Machine review of arXiv:2411.18940}
}
read the original abstract

Clinical language models are important for many applications in healthcare, but their development depends on access to extensive clinical text for pretraining. However, obtaining clinical notes from electronic health records (EHRs) at scale is challenging due to patient privacy concerns. In this study, we rephrase existing clinical notes using LLMs to generate synthetic pretraining corpora, drawing inspiration from previous work on rephrasing web data. We examine four popular small-sized LLMs (<10B) to create synthetic clinical text to pretrain both decoder-based and encoder-based language models. The method yields better results in language modeling and downstream tasks than previous synthesis approaches without referencing real clinical text. We find that augmenting original clinical notes with synthetic corpora from different LLMs improves performances even at a small token budget, showing the potential of this method to support pretraining at the institutional level or be scaled to synthesize large-scale clinical corpora.

Figures

Figures reproduced from arXiv: 2411.18940 by the authors.

Figure 1
Figure 1. Perplexity scores of language models pretrained on different synthetic sources. Ascplepius refers the [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Perplexity scores of language models pre [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Augmentation performance with synthetic data using different prompts. other hand, provide more stable benefits when com￾bined with original notes. These observations high￾light the lack of a single LLM that consistently outperforms others for handling clinical text. To further analyze the impact of prompts, we explore different prompt settings for each LLM for augmentation in [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

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    A literature review cataloging LLM-based augmentation methods across image, text, and speech, with a taxonomy of techniques, limitations, and suggested fixes.

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    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

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