{"id":"W4281251350","doi":"10.1002/sim.9441","title":"The polytomous discrimination index for prediction involving multistate processes under intermittent observation","year":2022,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Polytomous Rasch model; Multinomial logistic regression; Context (archaeology); Econometrics; Computer science; Logistic regression; Index (typography); Time horizon; Statistics; Machine learning; Mathematics; Item response theory; Psychometrics; Mathematical optimization","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001443991,0.0001371676,0.0002146545,0.00009573429,0.0004489704,0.00002687352,0.0001965917,0.00003018038,0.00006663085],"category_scores_gemma":[0.01336339,0.0000982078,0.00001440992,0.0002904478,0.0001683211,0.00005355932,0.00008297634,0.0002612049,4.689448e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002234295,"about_ca_system_score_gemma":0.00009144898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001169275,"about_ca_topic_score_gemma":0.0004008093,"domain_scores_codex":[0.998261,0.0001997809,0.0005854674,0.0002328159,0.0004708315,0.0002500461],"domain_scores_gemma":[0.9938864,0.00535566,0.0002423289,0.000189771,0.0002773149,0.00004855169],"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":[0.0001339159,0.000105574,0.001894723,0.0004377711,0.00002170005,0.000005784752,0.00266674,0.00013897,0.000147811,0.912854,0.007902377,0.07369061],"study_design_scores_gemma":[0.0009161617,0.0003941935,0.02180844,0.0001136565,0.00004459351,0.000005079135,0.004554369,0.08664857,0.00001914207,0.8844951,0.0008900465,0.0001106845],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004737131,0.0001604545,0.9913487,0.001240081,0.0009058067,0.0006825282,0.0007307416,0.00004006282,0.0001544296],"genre_scores_gemma":[0.7580779,0.0002018781,0.238156,0.0004793723,0.0002627156,0.001081402,0.0003800346,0.0000542852,0.001306441],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7533408,"threshold_uncertainty_score":0.9949475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1312956431616157,"score_gpt":0.4054855892397594,"score_spread":0.2741899460781436,"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."}}