{"id":"W2902576964","doi":"10.1002/cncy.22085","title":"The role of cytology in molecular testing and personalized medicine in lung cancer: A clinical perspective","year":2018,"lang":"en","type":"article","venue":"Cancer Cytopathology","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; St. Michael's Hospital","funders":"","keywords":"Medicine; Cytology; Lung cancer; Personalized medicine; Perspective (graphical); Precision medicine; Cancer; Molecular diagnostics; Medical physics; Intensive care medicine; Pathology; Internal medicine; Bioinformatics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003380363,0.0001158311,0.0004485921,0.0001134358,0.00005657007,0.000002420928,0.00006838636,0.0001024276,0.00005003092],"category_scores_gemma":[0.0005934591,0.00007820689,0.00003380515,0.0003022603,0.001160658,0.00002345844,0.00003890657,0.000203429,6.64112e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002894455,"about_ca_system_score_gemma":0.0004169976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004510004,"about_ca_topic_score_gemma":0.00285292,"domain_scores_codex":[0.9988142,0.0001567231,0.0003728836,0.0003192102,0.00008808928,0.0002489214],"domain_scores_gemma":[0.9990591,0.000344635,0.0001298073,0.0001690542,0.0002333544,0.00006399824],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003513168,0.0000643687,0.9686565,0.00002312374,0.00006421768,0.0001515916,0.004300165,0.000002847039,0.007260041,0.004645513,0.00004265331,0.01443766],"study_design_scores_gemma":[0.008948621,0.001384816,0.9730709,0.0004678137,0.0004118326,0.0002361139,0.006009642,0.001329949,0.001160155,0.00512992,0.001690134,0.0001600583],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9520551,0.03714522,0.00003666024,0.005310398,0.0002282362,0.0004254658,0.000009986123,0.00001258635,0.004776364],"genre_scores_gemma":[0.9970126,0.001505807,0.0003992224,0.0005651429,0.0002044061,0.0002070627,0.000001569144,0.00001401636,0.00009021335],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04495747,"threshold_uncertainty_score":0.6817807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324736420585666,"score_gpt":0.4239717983849526,"score_spread":0.400724434179096,"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."}}