{"id":"W2521059521","doi":"10.1002/cncr.30345","title":"Reply to Nomograms need to be presented in full","year":2016,"lang":"en","type":"letter","venue":"Cancer","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Bootstrapping (finance); Nomogram; Resampling; Statistics; Calibration; Selection (genetic algorithm); Econometrics; Medicine; Computer science; Mathematics; Machine learning; Oncology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01926724,0.001847591,0.002723318,0.002386508,0.003323538,0.005480878,0.005372362,0.03741399,0.01813605],"category_scores_gemma":[0.2367087,0.001505961,0.003601149,0.002623102,0.004075447,0.008419233,0.003733838,0.0506462,0.02773405],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004002479,"about_ca_system_score_gemma":0.004042888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003111192,"about_ca_topic_score_gemma":0.00287919,"domain_scores_codex":[0.9810517,0.006956229,0.003966002,0.00133001,0.005849328,0.0008467749],"domain_scores_gemma":[0.866742,0.05809919,0.006293182,0.005292264,0.05772234,0.005851104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002700409,0.000008081573,0.0001181814,0.00008422921,0.00001356676,0.000165073,0.00003363959,0.00005136312,0.0000477386,0.0006743036,0.994021,0.004755897],"study_design_scores_gemma":[0.00003228491,0.00003328586,0.000527598,0.000329536,0.0000226345,0.0008739138,0.0002428783,0.0003075236,0.0001754422,0.003497027,0.9938833,0.00007473017],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001246506,0.002057205,0.001125378,0.7035677,0.2920184,0.00004418865,0.0001174768,0.000166073,0.000778915],"genre_scores_gemma":[0.001359483,0.001708422,0.001354908,0.7514866,0.2385471,0.00014151,0.0001021459,0.0001477089,0.005152105],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.03741399,"threshold_uncertainty_score":0.1018962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1387783635278214,"score_gpt":0.4131201464076352,"score_spread":0.2743417828798138,"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."}}