{"id":"b89dc34f-013a-44e4-919d-b74a0311b996","arxiv_id":"2606.18471","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":6.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"A benchmark of 1,200 clinical documents shows three LLMs preserve diagnostic uncertainty expressions less than half the time and struggle with adjacent levels.","lead":"This paper builds a benchmark of 1,200 clinical documents with 9,184 uncertainty annotations across five levels and tests three LLMs on preserving those levels during text tasks. A smart generalist should read it because LLMs are entering clinical documentation where changing 'possible' to 'definite' can alter follow-up care and treatment decisions.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"Five-level annotation scheme lacks validation that distinctions affect clinical decisions","rationale":"This matches the reader's weakest assumption exactly. The provided input contains only the abstract, so no additional evidence on annotation validity is available; the concern therefore stands and the UNVERDICTED verdict is unchanged.","tokens_in":1661,"tokens_out":244,"duration_ms":18279,"concrete_test":"Survey 20 clinicians on 60 sentence pairs differing only by one adjacent uncertainty level; compute the fraction of cases where the clinician reports the change would alter management. If this fraction is <50%, the scheme's distinctions are not decision-relevant.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim (LLMs preserve uncertainty cues poorly, often <50% of the time) requires that the five-level scheme and 9,184 labels reflect distinctions that matter for real decisions such as testing or treatment. The abstract states the benchmark was constructed but supplies no evidence on level definitions, inter-annotator agreement, or clinician validation that adjacent levels (e.g., possible vs probable) produce different actions. Absent this, benchmark failures may not correspond to clinically relevant failures.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","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.","tokens_in":1723,"tokens_out":473,"duration_ms":16976,"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":[{"comment":"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.","section":"Methods"},{"comment":"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.","section":"Methods"},{"comment":"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.","section":"Results"}],"minor_comments":[{"comment":"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.","section":"Abstract"},{"comment":"Table or figure presenting per-level preservation rates is referenced but not described; ensure all result tables include row/column labels and sample sizes.","section":"Results"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"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.","responses":[{"response":"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_made":"partial","referee_comment":"[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."},{"response":"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_made":"yes","referee_comment":"[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."},{"response":"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_made":"yes","referee_comment":"[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."}],"tokens_in":1302,"tokens_out":501,"duration_ms":24461,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main thing to know is that the authors built a benchmark of 1,200 clinical documents with 9,184 five-level uncertainty annotations and found that three LLMs preserved the original cues less than half the time while mixing up adjacent categories.\n\nWhat is new is the dedicated focus on uncertainty preservation rather than fluency or coherence. The scale of the annotation set gives a concrete way to measure this specific failure mode that standard metrics miss.\n\nThe paper does a reasonable job flagging why this matters for decisions like testing or treatment.\n\nThe soft spot is the missing detail on how the annotations were produced. The abstract supplies no inter-annotator agreement numbers, no description of the level definitions, and no evidence that clinicians confirmed the distinctions change real actions. The stress-test concern holds: without that link, the reported failure rates may not track clinically relevant errors.\n\nThis is for people evaluating LLMs for clinical summarization or revision tasks. Readers who need safety-oriented benchmarks will see value in the idea even if the current write-up is thin on methods.\n\nIt deserves a serious referee because the problem is practical and the benchmark approach is direct, though the paper will need expanded methods and validation before it can be relied on.","headline":"This benchmark shows LLMs often drop or blur diagnostic uncertainty levels in clinical text, but the five-level labels lack shown clinical validation.","tokens_in":2184,"tokens_out":323,"would_cite":false,"duration_ms":25934,"reading_group":"maybe","serious_thinker":"unclear","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Large language models preserve diagnostic uncertainty levels in clinical text less than half the time.","keywords":["diagnostic uncertainty","clinical text","large language models","benchmark","uncertainty preservation","medical NLP","LLM evaluation"],"falsifier":"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.","tokens_in":2546,"feed_emoji":"🏥","tokens_out":542,"duration_ms":21750,"temperature":0.7,"pith_summary":"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.","feed_headline":"LLMs alter diagnostic uncertainty in over half of clinical cases","feed_subtitle":"Benchmark with 9,184 annotations shows models fail to keep possible versus definite distinctions intact.","key_machinery":"Five-level diagnostic uncertainty annotation scheme on clinical documents, used to score preservation by direct comparison of original and model-generated expressions.","core_discovery":"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.","pith_inferences":["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."],"forward_implications":["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."],"fun_headline_variants":["LLMs preserve diagnostic uncertainty in under half of instances","LLMs struggle with adjacent uncertainty levels in clinical text","Benchmark evaluates five levels of diagnostic uncertainty in LLMs","1200 documents benchmark LLM preservation of uncertainty expressions"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The five-level scheme and its 9,184 labels reflect distinctions in uncertainty that actually change clinical decisions.","fun_headline_variants_meta":{"raw":{"variants":["LLMs preserve diagnostic uncertainty in under half of instances","LLMs struggle with adjacent uncertainty levels in clinical text","Benchmark evaluates five levels of diagnostic uncertainty in LLMs","1200 documents benchmark LLM preservation of uncertainty expressions"]},"model":"grok-4.3","cost_usd":0.006997,"raw_usage":{"total_tokens":3200,"prompt_tokens":587,"num_sources_used":0,"completion_tokens":60,"cost_in_usd_ticks":69974500,"prompt_tokens_details":{"text_tokens":587,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2553,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":587,"tokens_out":60,"duration_ms":21528,"temperature":1.0,"reasoning_tokens":2553,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-27T00:21:37.581989+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"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.","supporting_citations":[],"review_version":1}