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arxiv: 2606.18471 · v1 · pith:FE27GOIF · submitted 2026-06-16 · cs.CL

Possible or Definite? A Benchmark for Evaluating Diagnostic Uncertainty Preservation in Clinical Text

Hongbo Du , Zixin Lu , Jiaming Qu This is my paper

Reviewed by Pith2026-06-27 00:21 UTCgrok-4.3pith:FE27GOIFopen to challenge →

classification cs.CL
keywords diagnostic uncertaintyclinical textlarge language modelsbenchmarkuncertainty preservationmedical NLPLLM evaluation
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The pith

Large language models preserve diagnostic uncertainty levels in clinical text less than half the time.

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

The paper builds a benchmark of 1,200 clinical documents carrying 9,184 annotations across five uncertainty levels to measure whether LLMs keep the original strength of diagnostic statements intact during summarization or revision. Phrases such as possible pneumonia versus definite pneumonia directly shape decisions on testing and treatment, so changing their level alters clinical meaning. Evaluation of three models shows they retain the original cues in fewer than half the cases and especially fail to separate adjacent levels. This gap is invisible to standard fluency or coherence metrics yet matters for any workflow that feeds LLM output to clinicians.

Core claim

The authors create a five-level uncertainty annotation scheme on 1,200 clinical documents and show that the three tested LLMs preserve the original uncertainty expressions in under half of instances while performing especially poorly on distinctions between neighboring levels.

What carries the argument

Five-level diagnostic uncertainty annotation scheme on clinical documents, used to score preservation by direct comparison of original and model-generated expressions.

If this is right

  • Standard fluency metrics miss clinically consequential changes in evidence strength.
  • LLM outputs for clinical summarization require explicit uncertainty checks before use.
  • Models need targeted training or constraints to maintain original uncertainty levels.
  • Deployment without such checks risks altered testing or treatment decisions.

Where Pith is reading between the lines

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

  • The benchmark could be applied to measure whether fine-tuning on uncertainty-labeled data improves preservation rates.
  • Similar failures may appear in non-English clinical notes or other medical specialties.
  • If clinicians routinely override changed uncertainty in practice, the safety impact may be smaller than the raw numbers suggest.

Load-bearing premise

The five-level scheme and its 9,184 labels reflect distinctions in uncertainty that actually change clinical decisions.

What would settle it

A test in which the same three LLMs preserve the original uncertainty level in more than half the benchmark cases, or in which clinicians judge that swapping adjacent levels does not affect their follow-up actions.

Figures

Figures reproduced from arXiv: 2606.18471 by Hongbo Du, Jiaming Qu, Zixin Lu.

Figure 1
Figure 1. Figure 1: Overview of the study workflow. The main goal of this study is to evaluate whether LLMs can correctly interpret uncertainty cues in clinical text. We do not evaluate LLMs on traditional measures such as generation quality, coherence, or factual accuracy. Instead, we study two complementary research questions (RQs): • RQ1 (Indirect assessment): Do LLMs preserve diagnostic uncertainty when trans￾forming clin… view at source ↗
read the original abstract

Large language models (LLMs) are increasingly used for clinical text tasks such as summarization and revision. While most studies evaluate the fluency and coherence of LLM-generated text, whether LLMs correctly preserve diagnostic uncertainty remains underexplored. In clinical practice, phrases such as ``possible pneumonia'' communicate the strength of available evidence and directly guide decisions about follow-up testing and treatment. Altering these uncertainty expressions can change the clinical meaning entirely. In this paper, we systematically evaluated this problem in two steps. First, we constructed a benchmark of 1,200 clinical documents with 9,184 uncertainty annotations across five levels. Second, we evaluated three LLMs on this benchmark. Our results show that (1) LLMs preserve the original uncertainty cues poorly, often less than half the time; (2) LLMs struggle with nuanced distinctions between adjacent levels. This work reveals a failure mode not captured by standard evaluation metrics and provides implications for the safe deployment of LLMs in clinical workflows.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The manuscript constructs a benchmark of 1,200 clinical documents containing 9,184 uncertainty annotations across five levels of diagnostic uncertainty. It then evaluates three LLMs on their ability to preserve these uncertainty expressions when processing the text, reporting that preservation occurs less than half the time and that models particularly struggle to maintain distinctions between adjacent levels.

Significance. If the benchmark's annotations are shown to reflect distinctions that affect real clinical decisions, the work identifies a failure mode in LLM-generated clinical text that is invisible to standard fluency or coherence metrics and has direct implications for safe deployment in summarization or revision tasks.

major comments (3)
  1. [Methods] Benchmark construction (Methods): the five-level scheme is presented as clinically meaningful, yet no evidence is supplied that adjacent levels (e.g., 'possible' vs. 'probable') produce different actions such as testing or treatment; without clinician validation or decision-impact data, the central claim that LLM failures are clinically consequential rests on an unverified assumption.
  2. [Methods] Annotation process (Methods): the abstract and evaluation sections report 9,184 labels but supply no inter-annotator agreement statistics, annotation guidelines, or clinician involvement details; these omissions prevent verification that the quantitative results (preservation <50 %) rest on reproducible, reliable labels.
  3. [Results] Model evaluation (Results): the paper states quantitative findings for three LLMs but provides neither the exact prompts used nor any statistical tests or confidence intervals; without these, the reported performance gaps cannot be assessed for robustness.
minor comments (2)
  1. [Abstract] The abstract claims 'often less than half the time' but does not define the exact metric (exact match, partial credit, etc.); a precise definition should appear in the evaluation protocol.
  2. [Results] Table or figure presenting per-level preservation rates is referenced but not described; ensure all result tables include row/column labels and sample sizes.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We address each major comment below and indicate the revisions we will make to the manuscript.

read point-by-point responses

  1. Referee: [Methods] Benchmark construction (Methods): the five-level scheme is presented as clinically meaningful, yet no evidence is supplied that adjacent levels (e.g., 'possible' vs. 'probable') produce different actions such as testing or treatment; without clinician validation or decision-impact data, the central claim that LLM failures are clinically consequential rests on an unverified assumption.

    Authors: We acknowledge that the manuscript does not supply new clinician validation or decision-impact data demonstrating that adjacent uncertainty levels lead to different clinical actions. The five-level scheme draws from standard clinical terminology used in diagnostic reporting, but we agree this assumption requires explicit support. In revision we will add a dedicated subsection in Methods citing existing literature on how uncertainty phrasing influences testing and treatment decisions, and we will add a limitations paragraph noting the absence of primary validation data in this study. revision: partial

  2. Referee: [Methods] Annotation process (Methods): the abstract and evaluation sections report 9,184 labels but supply no inter-annotator agreement statistics, annotation guidelines, or clinician involvement details; these omissions prevent verification that the quantitative results (preservation <50 %) rest on reproducible, reliable labels.

    Authors: We agree that inter-annotator agreement statistics, full annotation guidelines, and details of clinician involvement are necessary for reproducibility and were omitted from the initial submission. In the revised Methods section we will report these statistics (including Cohen’s kappa or equivalent), reproduce the annotation guidelines as an appendix, and clarify the roles and qualifications of the annotators. revision: yes

  3. Referee: [Results] Model evaluation (Results): the paper states quantitative findings for three LLMs but provides neither the exact prompts used nor any statistical tests or confidence intervals; without these, the reported performance gaps cannot be assessed for robustness.

    Authors: We agree that the exact prompts, statistical tests, and confidence intervals are required to evaluate robustness and were not included. In revision we will add the full prompts to an appendix and report appropriate statistical comparisons with confidence intervals in the Results section. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark and evaluation

full rationale

The paper constructs a benchmark via manual annotation of 1,200 documents into five uncertainty levels and then evaluates LLMs on preservation of those labels. No derivations, equations, fitted parameters, or predictions are present that could reduce to the inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The work is self-contained empirical measurement; the central claims rest on the annotation process and model outputs rather than any self-referential reduction. Absence of external validation for clinical impact is a separate assumption-validity concern, not circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No theoretical components; the work is an empirical benchmark study with no free parameters, axioms, or invented entities.

pith-pipeline@v0.9.1-grok · 5703 in / 974 out tokens · 21602 ms · 2026-06-27T00:21:37.581989+00:00 · methodology

discussion (0)

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Reference graph

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