{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06666175,0.002508707,0.002922952,0.006554042,0.001264776,0.003848184,0.002413482,0.008788151,0.005864888],"category_scores_gemma":[0.150627,0.001292185,0.003796671,0.006614334,0.005460745,0.005138126,0.003298855,0.01099401,0.001589134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003152095,"about_ca_system_score_gemma":0.003090811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002778887,"about_ca_topic_score_gemma":0.002026444,"domain_scores_codex":[0.9168733,0.07074995,0.003555502,0.001823931,0.006570729,0.0004264599],"domain_scores_gemma":[0.7470746,0.2403755,0.003154645,0.004043566,0.004799363,0.000552333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003945308,0.0002805424,0.00171446,0.007880609,0.0006849457,0.001139806,0.0006825331,0.01491198,0.000712476,0.6573476,0.09082799,0.2234226],"study_design_scores_gemma":[0.0005251786,0.0004090577,0.0006699176,0.005668255,0.0002249276,0.001115412,0.0001290807,0.03189163,0.0005454035,0.7832136,0.1753899,0.0002177025],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001126738,0.1051645,0.8374131,0.03821716,0.004516821,0.001037738,0.0004887152,0.0004316159,0.01160351],"genre_scores_gemma":[0.02195495,0.05705152,0.8904454,0.01611392,0.007602757,0.003849149,0.0002272552,0.0002906844,0.002464361],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06666175,"threshold_uncertainty_score":0.3525453,"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."}}