{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001705777,0.0000648479,0.0001420385,0.0002568445,0.00006129275,0.00001127869,0.0001911248,0.0000228085,0.0001757664],"category_scores_gemma":[0.002467979,0.0000326076,0.00001638664,0.0004874266,0.0005727721,0.0001784941,0.0000812471,0.0001179334,0.00003942986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001190737,"about_ca_system_score_gemma":0.00009293747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003104197,"about_ca_topic_score_gemma":0.00000225362,"domain_scores_codex":[0.9987205,0.00001383332,0.0001885264,0.000279393,0.0005508265,0.0002468989],"domain_scores_gemma":[0.9993793,0.0001359745,0.00003639949,0.0002288898,0.00006046826,0.000158941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002854651,0.00001449841,0.03003073,0.000005956511,6.479343e-7,0.00001629803,0.0001573482,0.000002128488,0.5354747,0.00016126,0.0002724776,0.4338354],"study_design_scores_gemma":[0.004810859,0.0006333859,0.8939879,0.003853746,0.00001322554,0.0002987972,0.0001344385,0.01580986,0.01234409,0.003575594,0.06428728,0.0002508094],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9726605,0.0001686495,0.01006103,0.01002009,0.0006411326,0.000117115,2.577627e-7,0.00003673099,0.006294536],"genre_scores_gemma":[0.9971035,0.0001159474,0.001557355,0.0004733258,0.0001509457,0.000002593164,2.015686e-7,0.000006013761,0.0005900987],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8639572,"threshold_uncertainty_score":0.295458,"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."}}