{"id":"0546fdaf-270f-4ba3-9b9d-1159ef89e78c","arxiv_id":"2510.03844","paper_version":1,"verdict":"CONDITIONAL","confidence":"MODERATE","novelty_score":5.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":1,"one_line_summary":"An ICD-10-based algorithm with an LLM-expanded, clinician-approved roadmap recovers as many missing allostatic-load measurements as expert chart reviews on 100 patients and scales to 1,000.","lead":"An algorithm that mines ICD-10 diagnosis codes recovers roughly the same amount of missing EHR data as manual chart reviews, and a large language model helped expand the code list. It matters because automated recovery could replace expensive chart review for scaling computable phenotypes like the allostatic load index.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The central claim of 'similar accuracy' is unsupported: the algorithm is compared to chart reviews only by aggregate recovery counts (45 vs 45), never per-data-point agreement, so 'accuracy' is never measured.","rationale":"The reader identified the ICD-10 proxy assumption as the weakest assumption, and that is indeed a substantive concern. However, the most load-bearing issue for the paper's central assertion ('similar accuracy to chart reviews') is that accuracy is never directly evaluated: the only quantitative comparison is aggregate recovered counts, not per-data-point agreement. The two 45s could be entirely different missing components, and the paper's own examples (e.g., CRP 'infection' matches deemed irrelevant by reviewers) show this is not a mere theoretical possibility. The reader's rationale also mentions this (compares counts rather than agreement), but the formal weakest_assumption field focuses on the proxy. I consider the evaluation gap to be the more direct and more consequential flaw, hence 'partial' agreement. The central claim could still survive if a confusion matrix showed high agreement, so this is an addressable empirical question rather than a fatal logical contradiction. That makes CONDITIONAL appropriate, which the reader already assigned; my stress test does not move the verdict. I also note the paper itself admits 'some of these matches were mistaken' (Results) without quantifying the error rate, which reinforces the need for a head-to-head accuracy assessment. A held-out validation would be ideal, but the immediate, decisive check is the 2x2 table on the existing 100 patients.","tokens_in":14173,"tokens_out":3545,"duration_ms":32600,"concrete_test":"Compute the 2x2 table for the 413 missing data points in the 100 chart-reviewed patients: algorithm's recovered/unhealthy vs chart-review's recovered/unhealthy (excluding protocol errors or treating them as missing per paper). Report sensitivity, specificity, positive predictive value, overall agreement, and Cohen's kappa, both overall and per component. Also report the number of data points recovered by both, algorithm-only, and chart-review-only. If PPV and agreement are high (e.g., kappa >0.8), the claim holds; if the algorithm's recovered set largely differs from chart review, the 'similar accuracy' statement is not supported.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The paper's core conclusion rests on the 100-patient chart-review comparison. In Results (Figure 1A-E), chart reviews recovered 45 of 413 missing components, and the LLM(context+clinicians) algorithm also recovered 45. But these are aggregate counts, not matches. The text itself describes disagreements: for homocysteine, chart reviews recovered 24 components the algorithm missed; for CRP, the algorithm recovered many values chart reviewers explicitly deemed irrelevant (e.g., urinary tract infection). This means the two 45s are not the same set, yet no confusion matrix, overlap count, sensitivity/PPV, or kappa is reported. Without per-data-point agreement, the statement 'similar accuracy to chart reviews' is not established — equal counts are consistent with completely disjoint recoveries. Additionally, the final roadmap was selected by clinician adjudication of LLM(context) additions after seeing these same 100 patients' ICD codes, so the recovered count is in-sample and likely optimistic. The weakness is not that the algorithm is useless; it is that the central accuracy claim is unmeasured. The fix is straightforward: cross-tabulate algorithm classifications against chart-review classifications for all 413 missing data points.","agreement_with_reader":"partial"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper proposes an algorithmic approach to recover missing allostatic load index (ALI) components in EHR data by matching ICD-10 codes against clinically derived search-term 'roadmaps.' The authors compare the recovery counts of several roadmap variants — the clinicians' original roadmap, a baseline LLM-generated roadmap, an LLM-generated roadmap seeded with the clinicians' terms (LLM(context)), and a clinician-adjudicated version of that roadmap — against expert chart reviews on 100 patients, and then apply the final roadmap to 1,000 patients. The abstract claims the algorithm achieves 'similar accuracy to chart reviews' based on aggregate recovery counts (45 of 413 missing components recovered by both chart reviews and the LLM(context+clinicians) algorithm). The paper includes reproducible R code and clear descriptions of the prompts and adjudication process.","tokens_in":14464,"tokens_out":2845,"duration_ms":23668,"significance":"If the central accuracy claim were properly supported, this work would be a useful contribution: a scalable, low-cost alternative to manual chart review for recovering missing EHR data, with potential applications to computable phenotyping and learning health systems. The manuscript is transparently written, provides all R code and prompts, and reports recovery counts stratified by component and by patient. However, the headline conclusion rests on aggregate recovery counts rather than per-data-point agreement, which is a critical gap. The current evidence does not establish 'similar accuracy'; it establishes 'similar number of recovered components,' which is a weaker and potentially misleading claim.","major_comments":[{"comment":"The central claim of 'similar accuracy to chart reviews' is not supported by the reported analyses. The comparison is made only on aggregate recovery counts: chart reviews recovered 45 of 413 missing components, and the LLM(context+clinicians) algorithm also recovered 45. No confusion matrix, overlap count, sensitivity, positive predictive value, or agreement statistic is reported. The text itself acknowledges substantial disagreement: chart reviews recovered 24 homocysteine components the algorithm missed, and the algorithm recovered many CRP values that chart reviewers deemed clinically irrelevant (e.g., urinary tract infection). Equal aggregate counts are consistent with completely disjoint recovery sets. Please provide a per-data-point cross-tabulation of algorithm versus chart-review classifications for all 413 missing data points and report sensitivity/PPV (or kappa), stratified by","section":"Results, 'Expert chart reviews versus missing data recovery algorithms' (Figure 1A–E)"},{"comment":"The comparison is not independent of the baseline. The LLM(context) prompt explicitly instructs the model to include the examples from the clinicians' original roadmap, so the LLM's term list is seeded with the very terms used in the baseline algorithm. More importantly, the final LLM(context+clinicians) roadmap was selected by clinician adjudication after examining the LLM(context) matches in the same 100 patients whose chart reviews are used as the gold standard, and the reported 45/45 recovery count is therefore an in-sample fit. This selection process likely inflates agreement. To support the accuracy claim, the roadmap adjudication should be performed (or at least evaluated) on a held-out set of patients, or the authors should report cross-validated estimates of agreement.","section":"Methods, 'Enhancements with large language models' (Prompt 2) and Results, 'Clinicians’ adjudication'"},{"comment":"The algorithm's core proxy — that a missing biomarker is 'unhealthy' whenever a matching ICD-10 diagnosis appears in the chart — is never validated against patients who have both a measured biomarker value and the corresponding diagnosis code(s). The CRP disagreement (e.g., urinary tract infection flagged by the algorithm but not by chart reviewers) demonstrates that the mapping is context-sensitive and can produce false positives. Without a validation study using patients with both measured values and diagnoses, the recovered 'unhealthy' labels, and any ALI estimates built on them, are at risk of systematic bias. Please validate the proxy in a subset of patients with non-missing measured values, reporting sensitivity and specificity of the diagnosis-code rule.","section":"Methods, 'Missing data recovery algorithms' and Results, 'Expert chart reviews versus missing data recovery algorithms'"}],"minor_comments":[{"comment":"Typo: 'plausability' should be 'plausibility'.","section":"Abstract"},{"comment":"Typo: 'rlectronic' should be 'electronic' in the title of the cited article.","section":"Reference [11]"},{"comment":"In the sentence 'LLM (baseline) only recovered three missing components (7%)', the percentage appears to be a numerical error. Three out of 413 missing components is approximately 0.7%, not 7%.","section":"Results, 'Expert chart reviews versus missing data recovery algorithms'"},{"comment":"The footnote for the LLM(context+clinicians) row ('*Clinicians were only asked to review ICD-10 codes from LLM(context) that matched patients in our sample; it was not possible for LLM(context+clinicians) to match more than 275 ICD-10 codes.') is useful but the asterisk placement in the table could be clearer; consider placing it directly on the '243' value or the row label.","section":"Table 2"},{"comment":"The description of the number of LLM generations (20) is present, but the choice of 20 is not justified. A short rationale (e.g., stability of the superset) would strengthen the reproducibility of the method.","section":"Methods, 'Enhancements with large language models'"}],"recommendation":"major_revision","confidential_remarks":"The paper addresses a practical and timely problem, and the authors have been unusually transparent about their prompts, code, and limitations. However, the central accuracy claim is currently supported only by aggregate recovery counts, not by per-data-point agreement. The in-sample selection of the final roadmap further undercuts the comparison. With a straightforward additional analysis (cross-tabulation, sensitivity/PPV, and ideally a held-out or cross-validated evaluation), the contribution could become publishable; as it stands, the main conclusion is premature."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Colleague,\n\nQuick take on Lotspeich et al. (arXiv:2510.03844). The new piece is real: using an LLM (with and without clinician context) to expand a clinician-derived search-term roadmap, then clinician adjudication, then applying that roadmap as an ICD-10 matching rule to recover missing ALI components. The comparison of four roadmaps on 100 chart-reviewed patients and the scale-up to 1000 patients is new, and the authors are transparent about the disagreements (e.g., CRP “infection” matches that chart reviewers deemed irrelevant). Code and supplement are available. That is a legitimate descriptive contribution.\n\nThe soft spot is exactly where the reader’s report lands. The central claim, “similar accuracy to chart reviews,” is not supported by the analyses shown. The paper compares counts: chart reviews recovered 45 of 413 missing components; the LLM(context+clinicians) algorithm also recovered 45. But these are not necessarily the same 45. The paper itself notes homocysteine: chart reviews recovered 24 that the algorithm missed; for CRP, the algorithm recovered many that chart reviewers considered not relevant. No confusion matrix, no overlap count, no sensitivity/PPV/kappa. Equal counts are consistent with completely disjoint recovery. So “accuracy” is never measured. The stress-test note is correct.\n\nSecond, the final roadmap was selected after seeing the same 100 patients’ ICD codes. The clinicians adjudicated the LLM(context) additions, and the team adopted it after observing recovery counts on this sample. So the 45/45 match is in-sample and likely optimistic. External validation on a holdout set, or at least a pre-specified roadmap, would make the comparison meaningful.\n\nThird, the underlying proxy — that an ICD-10 code matching a search term implies the missing biomarker would be unhealthy — is never validated against patients with both the code and a measured value. The UTI/CRP disagreement shows context-sensitivity. So the systematic bias in recovered labels is unknown.\n\nNone of this kills the paper. The descriptive finding that a simple ICD-10 matching algorithm can recover a similar number of missing ALI components as manual chart review, at a fraction of the cost, is worth reporting. But the abstract’s “similar accuracy” overstates what was measured. The fix is straightforward: cross-tabulate algorithm classifications against chart-review classifications for all 413 missing data points; report agreement, sensitivity, PPV. Also validate the proxy, ideally on a holdout set.\n\nWho benefits: researchers building computable phenotypes from EHRs, especially those using LLMs for term expansion. The paper deserves a serious referee, but it needs major revision before the accuracy claim is credible. I’d bring it to a reading group as a case study in evaluation pitfalls.\n\nRecommendation: send to peer review. The work is novel enough and the flaw is addressable.","headline":"A useful LLM-assisted roadmap for EHR missing-data recovery, but the 'similar accuracy' claim rests on aggregate counts, not per-data-point agreement — fixable with a confusion matrix.","tokens_in":14924,"tokens_out":2383,"would_cite":true,"duration_ms":27806,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"An automated ICD-10 algorithm using a clinician-approved, LLM-expanded roadmap recovers missing electronic health record values as accurately as expert manual chart review, and can be applied to every patient in a study.","keywords":["electronic health records","missing data","chart review","ICD-10","large language models","allostatic load","computable phenotype","data quality"],"falsifier":"Among patients who have both a non-missing ALI biomarker measurement and an ICD-10 code that matches the roadmap's search term for that component, compute the proportion whose measurement actually crosses the unhealthy threshold. If that positive predictive value is not well above the base rate in unmatched patients, the algorithm's mechanism — diagnosis code implies unhealthy missing value — is unsupported. A second check: re-run the full comparison on a fresh sample of, say, 200 chart-reviewed patients not used to build the roadmap; the algorithm should recover the same fraction as chart rev","tokens_in":14094,"feed_emoji":"🩺","tokens_out":5858,"duration_ms":123360,"temperature":0.7,"pith_summary":"The paper sets out to replace expensive, slow manual chart review with an automated algorithm that recovers missing values in electronic health records. The algorithm checks whether a patient's ICD-10 diagnosis codes match 'search terms' in a clinical roadmap built for each missing biomarker, and a match flags the missing value as unhealthy. To build the roadmap, a large language model proposed many new terms, which clinicians then pruned to keep only those they judged clinically relevant. In 100 patients whose charts had been reviewed by experts, the final algorithm recovered the same number of missing values (45 of 413) as the human reviewers. Applied automatically to all 1,000 patients, it recovered 531 of 4,009 missing components — recovery that would otherwise have required roughly 100 hours of manual review per 100 patients.","feed_headline":"LLM-guided algorithm matches chart review for missing EHR data","feed_subtitle":"It recovered the same 45 values as human reviewers in 100 patients, then ran automatically on 1,000.","key_machinery":"The central object is the 'roadmap': a table linking each of the ten allostatic load index (ALI) components — a composite 'wear and tear' score from cardiovascular, metabolic, and inflammatory biomarkers — to clinical search terms (e.g., 'infection' for C-reactive protein, 'diabetes' for hemoglobin A1c). The algorithm merges that roadmap against each patient's ICD-10 diagnosis codes and treats any matched code as evidence that a missing biomarker value would be unhealthy. The LLM's role is to generate a broad superset of candidate search terms, which clinicians then review for relevance; the final roadmap is the clinician-approved subset. The comparison benchmark is expert manual chart revie","core_discovery":"The central claim is that a clinically-driven, LLM-enhanced missing data recovery algorithm based on ICD-10 codes can recover missing EHR data with accuracy similar to expert chart reviews, at a fraction of the cost, and can be applied to an entire study sample. On the 100-patient validation subset, the final roadmap — the LLM's context-aware suggestions filtered by clinician adjudication — recovered 45 of 413 missing ALI components, exactly matching the number recovered by manual chart review. In the full 1,000-patient sample, the same algorithm recovered 531 of 4,009 missing components, raising the median number of non-missing ALI components per patient from 6 to 7. The authors further sho","pith_inferences":["The key unvalidated proxy — that a matching ICD-10 code implies a missing lab would have been unhealthy — could be tested directly: among patients who have both the code and a measured value, the code's positive predictive value for an unhealthy measurement should be computed. If it is low, the recovered labels are noisy.","The final algorithm's exact match with chart review (45/413) hides the fact that unpruned roadmaps over-recovered CRP values (e.g., urinary tract infections flagged as infections); agreement therefore depends on how aggressively clinicians prune, and a different adjudication could change the recovered set.","Because the LLM's baseline prompt (no context) produced far worse coverage, the method's performance is sensitive to prompt design and model choice; a testable extension is to compare several LLMs and prompt strategies on the same validation set.","The binary 'unhealthy' recovery ignores magnitude; coupling this algorithm with existing EHR lab values could recover approximate continuous values rather than only thresholds, supporting different downstream analyses."],"forward_implications":["Missing-data recovery for EHR-based phenotypes no longer has to be limited to small chart-reviewed subsamples; the algorithm can be run on every patient in a cohort.","Whole-sample ALI estimates can be computed with less missingness, and the association between ALI and outcomes such as healthcare engagement closely resembles the chart-review-based estimates.","The roadmap logic is simple enough to encode directly in SQL, so it can be embedded in EHR data pipelines and reused for other computable phenotypes.","Because the algorithm only needs structured ICD-10 codes and no protected health information is sent to the LLM, the approach can be adopted in settings where privacy constraints would block text-based review.","The same pattern — LLM proposes, clinicians approve — could be extended to other data-quality dimensions like correctness and plausibility."],"fun_headline_variants":["LLM-enhanced algorithm recovers missing EHR data like chart review","Automated LLM-enhanced tool matches chart review for missing EHR data","ICD-10 roadmap with LLM additions recovers missing EHR data accurately","LLM-refined roadmap recovers missing EHR data at chart-review accuracy"],"cache_read_input_tokens":2304,"weakest_assumption_plain":"The load-bearing assumption is that a patient who has an ICD-10 diagnosis code matching a roadmap search term (e.g., 'infection' for CRP) would, if tested, have had an unhealthy value of the missing biomarker; this mapping is never validated against patients who have both the code and a measured value.","fun_headline_variants_meta":{"raw":{"variants":["LLM-enhanced algorithm recovers missing EHR data like chart review","Automated LLM-enhanced tool matches chart review for missing EHR data","ICD-10 roadmap with LLM additions recovers missing EHR data accurately","LLM-refined roadmap recovers missing EHR data at chart-review accuracy"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000312,"raw_usage":{"total_tokens":1659,"prompt_tokens":835,"completion_tokens":824,"prompt_tokens_details":{"cached_tokens":256},"prompt_cache_hit_tokens":256,"prompt_cache_miss_tokens":579,"completion_tokens_details":{"reasoning_tokens":747}},"tokens_in":579,"tokens_out":824,"duration_ms":6527,"temperature":1.0,"reasoning_tokens":747,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-04T11:34:10.638048+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Among patients who have both a non-missing ALI biomarker measurement and an ICD-10 code that matches the roadmap's search term for that component, compute the proportion whose measurement actually crosses the unhealthy threshold. If that positive predictive value is not well above the base rate in unmatched patients, the algorithm's mechanism — diagnosis code implies unhealthy missing value — is unsupported. A second check: re-run the full comparison on a fresh sample of, say, 200 chart-reviewed patients not used to build the roadmap; the algorithm should recover the same fraction as chart rev","supporting_citations":[],"review_version":1}