{"id":"W2555459351","doi":"10.12688/f1000research.8680.1","title":"Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration","year":2016,"lang":"en","type":"preprint","venue":"F1000Research","topic":"Statistical Methods in Clinical Trials","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Economics","funders":"Deutsche Forschungsgemeinschaft; European Commission","keywords":"Interim; Prostate cancer; Automatic summarization; Clinical trial; Computer science; Data collection; Medicine; Data science; Medical physics; Artificial intelligence; Cancer; Statistics; Internal medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05297675,0.002247009,0.002247061,0.004478205,0.00156337,0.007384847,0.004008627,0.005402561,0.005641095],"category_scores_gemma":[0.1555125,0.001140387,0.002462945,0.004211321,0.01105445,0.01146009,0.007609772,0.01019787,0.002694948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002658564,"about_ca_system_score_gemma":0.004229987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002054016,"about_ca_topic_score_gemma":0.002014971,"domain_scores_codex":[0.9660147,0.02523387,0.001889534,0.002793345,0.003680844,0.0003877363],"domain_scores_gemma":[0.8738413,0.09602986,0.006944654,0.01521059,0.006126608,0.001846857],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002611043,0.0001102548,0.004229179,0.00161324,0.000408454,0.0001401213,0.001345812,0.03179206,0.001138351,0.6358508,0.06719799,0.2559125],"study_design_scores_gemma":[0.00005130946,0.00008073543,0.000919949,0.0003891967,0.00003333718,0.00009817646,0.0001658814,0.05292479,0.0008913416,0.9160939,0.028274,0.00007738717],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002329049,0.002653523,0.9552609,0.03406431,0.0006037698,0.0002043746,0.0009953774,0.0007711041,0.003117623],"genre_scores_gemma":[0.08854984,0.003518649,0.8881626,0.01010339,0.003202636,0.001400007,0.001471182,0.0006048784,0.002986893],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9470233,"threshold_uncertainty_score":0.2801712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6250446288852266,"score_gpt":0.6063474123778936,"score_spread":0.01869721650733303,"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."}}