{"id":"W2480199118","doi":"10.1126/science.aaf5101","title":"Countering imprecision in precision medicine","year":2016,"lang":"en","type":"article","venue":"Science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Genome Alberta; Genome Canada","keywords":"Psychological intervention; Precision medicine; Computer science; Data science; Medicine; Pathology; Nursing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06406766,0.00130042,0.002944816,0.003025305,0.001657588,0.01107227,0.002322159,0.003601508,0.00479112],"category_scores_gemma":[0.2972285,0.001215316,0.001016446,0.001959978,0.01188468,0.0114505,0.007485557,0.006320507,0.0006882823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003757528,"about_ca_system_score_gemma":0.005862631,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003108672,"about_ca_topic_score_gemma":0.00181682,"domain_scores_codex":[0.9468045,0.03240968,0.004119663,0.004897085,0.01057648,0.001192612],"domain_scores_gemma":[0.632654,0.2855782,0.02839807,0.03507734,0.0162336,0.002058916],"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":[0.001869834,0.0002846597,0.02458799,0.002344707,0.00130875,0.0003795628,0.005808915,0.07243277,0.003087119,0.3838882,0.009168942,0.4948385],"study_design_scores_gemma":[0.0003106932,0.000554616,0.008559707,0.001499776,0.0004416942,0.00062465,0.001541447,0.04377173,0.005393607,0.8907115,0.04633593,0.0002547094],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.06381653,0.02827576,0.794274,0.07649923,0.002363498,0.0002452296,0.0003256833,0.0009432284,0.03325681],"genre_scores_gemma":[0.8171071,0.005549713,0.1651046,0.007506903,0.001899527,0.0003097781,0.0001048124,0.000328734,0.002088803],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.06406766,"threshold_uncertainty_score":0.3388262,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0140137833949218,"score_gpt":0.3420158900937316,"score_spread":0.3280021066988098,"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."}}