{"id":"W1980062933","doi":"10.1002/widm.1047","title":"Machine learning methods for predicting tumor response in lung cancer","year":2012,"lang":"en","type":"article","venue":"Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery","topic":"Lung Cancer Treatments and Mutations","field":"Medicine","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Radiation therapy; Lung cancer; Medicine; Cancer; Personalization; Treatment of lung cancer; Radiation treatment planning; Intensive care medicine; Oncology; Bioinformatics; Internal medicine; Computer science; Biology","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":[0.002727602,0.0002761991,0.0006735657,0.0001808571,0.0002570645,0.00006008275,0.0001862248,0.00005986367,0.00003494956],"category_scores_gemma":[0.0007475543,0.0002052625,0.0001100796,0.0002464354,0.00006762057,0.0009861398,0.0008734477,0.0002259374,0.000004273561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002032492,"about_ca_system_score_gemma":0.0001882012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002468864,"about_ca_topic_score_gemma":0.00006068726,"domain_scores_codex":[0.9979325,0.0004582037,0.0005902332,0.0005107708,0.00007051689,0.0004377642],"domain_scores_gemma":[0.9983672,0.0006671356,0.0002020283,0.0005434819,0.00004053043,0.000179624],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002913638,0.0007381823,0.4744451,0.002983312,0.0003965661,0.00001595493,0.02439304,0.000005422578,0.0009136294,0.0000337674,0.009401577,0.4837598],"study_design_scores_gemma":[0.01096563,0.002285998,0.09149513,0.03984348,0.005403908,0.0005664996,0.01190332,0.1382879,0.0002939555,0.0000539336,0.697205,0.00169524],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.298011,0.6872854,0.008228067,0.0006786979,0.00113894,0.002100317,0.0006702499,0.00007521502,0.001812159],"genre_scores_gemma":[0.8917029,0.02515827,0.06658264,0.0003221165,0.001671722,0.002290106,0.003268272,0.0001764529,0.008827564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6878034,"threshold_uncertainty_score":0.8370363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07363155616305792,"score_gpt":0.4794801865354944,"score_spread":0.4058486303724365,"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."}}