{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003869288,0.0006761898,0.0009827357,0.002768657,0.0003162855,0.001141655,0.001020702,0.001271751,0.001191169],"category_scores_gemma":[0.01410331,0.0002501648,0.0008024704,0.00170817,0.0005014232,0.0006921692,0.000628347,0.001488588,0.0005801561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008746497,"about_ca_system_score_gemma":0.0009574814,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003635101,"about_ca_topic_score_gemma":0.001934324,"domain_scores_codex":[0.9985713,0.0007072975,0.0001497894,0.0002307928,0.0002618402,0.00007901019],"domain_scores_gemma":[0.9899645,0.008491483,0.0006808368,0.0002336962,0.0005227779,0.0001066312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001698631,0.0002282886,0.03114075,0.000237756,0.0002073078,0.0001460919,0.00005043103,0.7214701,0.0008340065,0.00537923,0.003149405,0.2369868],"study_design_scores_gemma":[0.000006952106,0.0000252166,0.001091064,0.00001890679,0.000009131036,0.00002820303,0.00000937067,0.9926106,0.0002314015,0.005582015,0.000381553,0.000005580093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09847089,0.008735287,0.882654,0.00332988,0.0002697842,0.0003326437,0.001507387,0.001202537,0.003497515],"genre_scores_gemma":[0.8153774,0.002175735,0.1771834,0.0004448679,0.00037487,0.0004826819,0.001829793,0.00006741039,0.002063831],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003869288,"threshold_uncertainty_score":0.02046305,"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."}}