{"id":"84690206-f1fa-40a5-89ae-4348bf4bcc8b","arxiv_id":"2606.19852","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"high","formal_verification":"none","parameter_count":0,"one_line_summary":"Zero-shot agentic LLMs extract 13 CAP synoptic fields from lung pathology reports at Micro-F1 0.893, approaching a supervised GatorTron baseline of 0.960 without task-specific training.","lead":"This paper tests a zero-shot agentic LLM workflow to extract 13 structured fields from lung pathology reports, reaching Micro-F1 0.893 versus a supervised baseline at 0.960. A smart generalist might read it to see whether generative models can cut the cost of manual clinical data extraction for cancer registries.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"Registry-aligned evaluation may not ensure apples-to-apples comparison if LLM agentic outputs receive different parsing or alignment rules than the supervised NER-RE baseline","rationale":"The reader's weakest assumption directly identifies the load-bearing point for the central claim. With full text now available the evaluation details can be inspected, but the concern remains structural rather than resolved by additional description alone.","tokens_in":1693,"tokens_out":290,"duration_ms":23555,"concrete_test":"Recompute both Micro-F1 scores using identical field-level exact-match rules and the same input report set, with no additional post-processing or alignment steps applied only to the LLM outputs; if the LLM score drops below 0.85 the competitiveness claim weakens.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The headline claim that zero-shot agentic LLMs are a competitive low-cost alternative rests on the Micro-F1 gap (0.893 vs 0.960) being meaningful. The supervised GatorTron baseline uses an NER-RE pipeline whose errors are measured at the entity/relation level; the LLM workflow populates 13 CAP fields via planning and extraction steps. If the novel registry-aligned framework applies looser string/semantic matching, allows multi-step correction, or normalizes outputs differently for the generative case, the scores are not directly comparable and the performance delta cannot be attributed to the zero-shot approach itself.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper claims to develop a zero-shot agentic LLM workflow that populates 13 College of American Pathologists (CAP) synoptic fields from lung resection pathology reports. Five open-source generative LLMs are evaluated against a supervised GatorTron NER-RE baseline using a novel registry-aligned evaluation framework, reporting Micro-F1 scores of 0.960 (baseline) versus 0.893 (best LLM, GPT-OSS-20B with recall 0.949), and concludes that such models show potential as a low-cost alternative for lung pathology information extraction.","tokens_in":1823,"tokens_out":498,"duration_ms":11266,"significance":"If the evaluation framework produces a fair, apples-to-apples comparison, the work would demonstrate that zero-shot agentic LLMs can achieve competitive performance on structured clinical extraction tasks without task-specific training or annotation, offering a practical low-cost alternative to supervised pipelines for cancer registry population and staging.","major_comments":[{"comment":"Abstract: the central performance comparison (Micro-F1 0.893 vs 0.960) is presented without any dataset size, number of reports, definitions of the 13 CAP fields, or description of the agentic workflow steps (planning/extraction), rendering the claim unverifiable from the provided text and blocking assessment of reproducibility.","section":"Abstract"},{"comment":"Evaluation Framework (implied in methods/results): the registry-aligned framework is load-bearing for attributing the performance gap to the zero-shot approach, yet no explicit description is given of how LLM-generated outputs are parsed, string-matched, or semantically aligned versus the entity/relation-level errors of the NER-RE baseline; if matching rules differ (e.g., allowing multi-step correction or looser normalization for generative outputs), the scores are not directly comparable.","section":"Evaluation Framework"},{"comment":"Results: no error analysis or breakdown by field (especially for complex relations like Pathologic Stage) is supplied, which is required to substantiate the claim that the LLM 'accurately extracting complex relations' without task-specific training.","section":"Results"}],"minor_comments":[{"comment":"Abstract: 'Micro-F1of' is missing a space before 0.960.","section":"Abstract"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for their constructive comments. We address each major comment below and indicate the revisions we will make to improve the manuscript.","responses":[{"response":"We agree that the abstract should be more self-contained to support verifiability. In the revised manuscript we will expand the abstract to report the number of lung resection pathology reports in the dataset, provide a brief enumeration or definition of the 13 CAP synoptic fields, and include a concise description of the agentic workflow steps (prompt, plan, extract).","revision_made":"yes","referee_comment":"[Abstract] Abstract: the central performance comparison (Micro-F1 0.893 vs 0.960) is presented without any dataset size, number of reports, definitions of the 13 CAP fields, or description of the agentic workflow steps (planning/extraction), rendering the claim unverifiable from the provided text and blocking assessment of reproducibility."},{"response":"The Methods section introduces the registry-aligned evaluation framework, but we acknowledge that the parsing, string-matching, and semantic alignment steps for LLM outputs require more explicit detail to demonstrate comparability with the NER-RE baseline. We will revise the Methods to add a dedicated subsection describing the exact procedures for parsing generative outputs, normalization rules, and matching criteria, and we will explicitly state that identical alignment rules are applied to both the LLM and baseline outputs.","revision_made":"yes","referee_comment":"[Evaluation Framework] Evaluation Framework (implied in methods/results): the registry-aligned framework is load-bearing for attributing the performance gap to the zero-shot approach, yet no explicit description is given of how LLM-generated outputs are parsed, string-matched, or semantically aligned versus the entity/relation-level errors of the NER-RE baseline; if matching rules differ (e.g., allowing multi-step correction or looser normalization for generative outputs), the scores are not directly comparable."},{"response":"We agree that an error analysis and field-level breakdown would strengthen the results section and better support the claim regarding complex relations. In the revised manuscript we will add an error analysis subsection that includes per-field Micro-F1 scores and qualitative discussion of errors, with focused attention on Pathologic Stage and other complex relations.","revision_made":"yes","referee_comment":"[Results] Results: no error analysis or breakdown by field (especially for complex relations like Pathologic Stage) is supplied, which is required to substantiate the claim that the LLM 'accurately extracting complex relations' without task-specific training."}],"tokens_in":1436,"tokens_out":546,"duration_ms":25706,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The paper shows a concrete zero-shot workflow using prompt-plan-extract steps on five open-source LLMs to fill 13 College of American Pathologists synoptic fields from lung resection reports. The best model lands at 0.893 Micro-F1 with 0.949 recall, which is close enough to the supervised baseline to matter for registry work where annotation is expensive.\n\nThe new piece is the direct head-to-head on these exact fields with open-source models and the registry-aligned scoring. It does a clean job of showing that agentic prompting can handle relations like pathologic stage without any task-specific training data.\n\nThe main soft spot is the evaluation setup. The abstract and stress-test note both flag that we need to see exactly how the 13 fields are scored for the generative outputs versus the NER-RE pipeline. If string matching or normalization rules differ, the 0.067 gap cannot be read as a pure zero-shot versus supervised result. Dataset size, report count, and error breakdown are also missing from the summary, which makes it hard to judge how stable the numbers are.\n\nThis is for clinical NLP groups and cancer registry teams who want low-annotation options. A reader already working on pathology extraction will get usable workflow ideas and a sense of current open-source performance.\n\nIt deserves peer review. The application is timely and the numbers are worth checking in detail, even if the methods section will need expansion on the evaluation rules.","headline":"Zero-shot agentic LLMs reach 0.893 Micro-F1 on 13 CAP lung fields versus 0.960 for the GatorTron baseline, but the comparison hinges on whether the registry-aligned eval treats both systems the same way.","tokens_in":2348,"tokens_out":388,"would_cite":false,"duration_ms":12699,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Zero-shot agentic LLMs populate 13 CAP fields from lung reports at 0.893 Micro-F1 without training.","keywords":["zero-shot learning","large language models","information extraction","pathology reports","lung cancer","agentic workflows","clinical narratives","cancer registry"],"falsifier":"A manual gold-standard review on a new collection of lung resection reports where the best zero-shot model's Micro-F1 on the 13 fields falls substantially below 0.893.","tokens_in":2598,"feed_emoji":"🧪","tokens_out":665,"duration_ms":24715,"temperature":0.7,"pith_summary":"This paper develops a zero-shot agentic workflow that applies open-source generative LLMs to extract structured data for 13 College of American Pathologists synoptic fields from lung resection pathology reports. It evaluates five such models against a supervised GatorTron NER-RE baseline using a registry-aligned framework, with the top model reaching Micro-F1 of 0.893 and recall of 0.949 while handling relations like pathologic stage. A sympathetic reader would care because manual extraction for cancer registries is labor-intensive and error-prone, and supervised pipelines demand expensive annotations plus risk cascading failures. The results position these LLMs as a potential low-cost alternative for information extraction from clinical narratives.","feed_headline":"Zero-shot LLMs extract lung pathology at 0.893 Micro-F1","feed_subtitle":"Open-source agentic models fill 13 CAP fields from reports without training data, approaching supervised baseline performance.","key_machinery":"The zero-shot agentic workflow that decomposes extraction into prompt, plan, and extract steps using generative LLMs to fill the 13 CAP fields directly from narrative text.","core_discovery":"By applying a zero-shot agentic workflow to five open-source LLMs, the study populates the 13 College of American Pathologists synoptic fields from lung resection pathology reports, with the best model (GPT-OSS-20B) achieving a Micro-F1 of 0.893 and recall of 0.949, closely approaching the supervised GatorTron baseline's 0.960 while accurately handling complex relations such as Pathologic Stage.","pith_inferences":["The same workflow structure could be tested on pathology reports from other cancer sites to check transferability.","Integration with existing registry systems might reduce the time and cost of manual abstraction.","Performance on rarer or more complex fields could be measured separately to identify remaining gaps."],"forward_implications":["The workflow avoids the need for expensive manual annotation required by supervised NER-RE pipelines.","Cascading failures from missed upstream entities in traditional methods are reduced.","Open-source models can serve as a low-cost solution for extracting lung pathology information.","Complex relations such as pathologic stage can be extracted accurately without task-specific training."],"fun_headline_variants":["Zero-shot LLMs hit 0.893 Micro-F1 on lung pathology reports","Agentic workflow extracts 13 CAP fields at 0.893 F1 zero-shot","Open LLMs reach 0.893 Micro-F1 without training on lung reports","GPT-OSS-20B scores 0.893 Micro-F1 on zero-shot pathology extraction"],"cache_read_input_tokens":64,"weakest_assumption_plain":"The novel registry-aligned evaluation framework produces an unbiased, apples-to-apples comparison between the zero-shot LLM outputs and the supervised GatorTron baseline on the 13 CAP fields.","fun_headline_variants_meta":{"raw":{"variants":["Zero-shot LLMs hit 0.893 Micro-F1 on lung pathology reports","Agentic workflow extracts 13 CAP fields at 0.893 F1 zero-shot","Open LLMs reach 0.893 Micro-F1 without training on lung reports","GPT-OSS-20B scores 0.893 Micro-F1 on zero-shot pathology extraction"]},"model":"grok-4.3","cost_usd":0.005271,"raw_usage":{"total_tokens":2552,"prompt_tokens":672,"num_sources_used":0,"completion_tokens":89,"cost_in_usd_ticks":52712000,"prompt_tokens_details":{"text_tokens":672,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1791,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":672,"tokens_out":89,"duration_ms":12529,"temperature":1.0,"reasoning_tokens":1791,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-26T17:47:18.067166+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A manual gold-standard review on a new collection of lung resection reports where the best zero-shot model's Micro-F1 on the 13 fields falls substantially below 0.893.","supporting_citations":[],"review_version":1}