{"id":"W3108873142","doi":"10.1002/mgg3.1554","title":"Matching methods in precision oncology: An introduction and illustrative example","year":2020,"lang":"en","type":"article","venue":"Molecular Genetics & Genomic Medicine","topic":"Cancer Genomics and Diagnostics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency; Canada's Michael Smith Genome Sciences Centre; Spinal Cord Injury BC; University of British Columbia; Canadian Centre for Applied Research in Cancer Control","funders":"Canadian Institutes of Health Research","keywords":"Propensity score matching; Precision medicine; Medicine; Hazard ratio; Precision oncology; Oncology; Matching (statistics); Cohort; Internal medicine; Randomized controlled trial; Personalized medicine; Clinical trial; Meta-analysis; Clinical Oncology; Proportional hazards model; Average treatment effect; Bioinformatics; Cancer; Confidence interval; Biology; Pathology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005629145,0.0002272488,0.0003197177,0.0000724139,0.00004708218,0.0000192587,0.000198259,0.0001835335,0.00003679556],"category_scores_gemma":[0.0001787662,0.0002263384,0.00003648938,0.0001357858,0.0001584555,0.00000398481,0.0002034983,0.0001841477,0.000002737711],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000537092,"about_ca_system_score_gemma":0.0001279123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001795613,"about_ca_topic_score_gemma":0.0001239443,"domain_scores_codex":[0.9982378,0.0002604859,0.0004069688,0.0007021163,0.0001231658,0.0002694401],"domain_scores_gemma":[0.9991225,0.0000450528,0.0001233965,0.000373752,0.00006890266,0.0002664113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001266388,0.00003252448,0.0002356553,0.00001489256,0.0000282662,0.00001713526,0.001916094,0.002041193,0.9545587,0.0004171067,0.0003193197,0.04029242],"study_design_scores_gemma":[0.00529383,0.007675484,0.01011448,0.00003923562,0.0001996685,0.0001276777,0.003466782,0.004768011,0.4658579,0.005643176,0.4957597,0.001054078],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8593397,0.004847389,0.1306867,0.004271761,0.0002336582,0.0003469673,0.000008195895,0.0000103246,0.0002552902],"genre_scores_gemma":[0.9247988,0.002358838,0.06828948,0.002934536,0.001325204,0.00002567159,0.0001851585,0.00005422351,0.00002810931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4954404,"threshold_uncertainty_score":0.9229812,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03180028813504936,"score_gpt":0.349710170342776,"score_spread":0.3179098822077266,"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."}}