{"id":"W4220659516","doi":"10.1016/j.jtho.2022.02.013","title":"NSCLC Subtyping in Conventional Cytology: Results of the International Association for the Study of Lung Cancer Cytology Working Group Survey to Determine Specific Cytomorphologic Criteria for Adenocarcinoma and Squamous Cell Carcinoma","year":2022,"lang":"en","type":"article","venue":"Journal of Thoracic Oncology","topic":"AI in cancer detection","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sunnybrook Health Science Centre; Health Sciences Centre; Université Laval; Princess Margaret Cancer Centre","funders":"Merck Sharp and Dohme; Daiichi-Sankyo; Takeda Pharmaceutical Company; AbbVie; Chugai Pharmaceutical; Roche; Novartis; GlaxoSmithKline; Amgen; Pfizer; Bayer; AstraZeneca; Eli Lilly and Company; University of Missouri; U.S. Department of Defense","keywords":"Subtyping; Adenocarcinoma; Cytology; Medicine; Lung cancer; Gold standard (test); Oncology; Carcinoma; Algorithm; Internal medicine; Cancer; Pathology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001966206,0.0003121599,0.000364915,0.001392657,0.0003341187,0.0004286099,0.000524134,0.000479868,0.0006721465],"category_scores_gemma":[0.004003169,0.0002741165,0.0004368105,0.001447026,0.0002895418,0.0005459436,0.000632418,0.000394694,0.0002725002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003517083,"about_ca_system_score_gemma":0.0005050123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01466979,"about_ca_topic_score_gemma":0.01615746,"domain_scores_codex":[0.9990356,0.00019584,0.0001745525,0.0001187586,0.0003948711,0.00008035663],"domain_scores_gemma":[0.9977603,0.0003905873,0.0007706825,0.0001261899,0.0007003312,0.0002519334],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000250752,0.00001679417,0.9989644,0.00000747449,0.00001308717,0.00001056571,0.00004233605,0.00001873773,0.0001925521,0.000003969187,0.0001336359,0.0005713461],"study_design_scores_gemma":[0.000002751702,0.0000318608,0.9994265,0.000004410234,0.00000988816,0.00005221317,0.0001281261,0.00009781925,0.0001015055,0.000006284524,0.0001376505,0.000001154239],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964504,0.0002630299,0.0003157406,0.00007582763,0.00001618218,0.00006794441,0.001877463,0.000009689399,0.0009237445],"genre_scores_gemma":[0.9975643,0.0001901389,0.0002721464,0.0000887427,0.00001080635,0.000043993,0.001558349,0.000006200065,0.000265293],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01466979,"threshold_uncertainty_score":0.02916878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06734476077710362,"score_gpt":0.3816303051502155,"score_spread":0.3142855443731119,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}