{"id":"W4225157559","doi":"10.1016/j.wneu.2022.02.030","title":"Classical Regression and Predictive Modeling","year":2022,"lang":"en","type":"article","venue":"World Neurosurgery","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Regression; Regression analysis; Context (archaeology); Predictive modelling; Medicine; Econometrics; Machine learning; Artificial intelligence; Computer science; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.04196,0.0001856132,0.001604373,0.000582961,0.0004559516,0.0003987607,0.0006259207,0.00001819363,0.005728791],"category_scores_gemma":[0.009903602,0.00009545378,0.0007730636,0.001624767,0.00004123909,0.0001537139,0.0005030347,0.0003082742,0.0001712943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002149912,"about_ca_system_score_gemma":0.00003289538,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000121435,"about_ca_topic_score_gemma":0.000002485443,"domain_scores_codex":[0.9824561,0.008150495,0.003999323,0.0009421525,0.004238338,0.0002135571],"domain_scores_gemma":[0.9922309,0.004287231,0.001471152,0.001695481,0.000159638,0.0001555945],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008333445,0.0001463834,0.08735108,0.00002264315,0.00007581798,0.0001248554,0.0004198399,0.02232732,0.0007313096,0.002287155,0.8589132,0.02751709],"study_design_scores_gemma":[0.0000835784,0.00002622008,0.003926829,0.00001240066,0.00005658039,0.00003756647,0.0001858395,0.6514595,0.00001302482,0.0039425,0.3401133,0.0001426374],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603284,0.001459808,0.01058766,0.007563296,0.002202247,0.0006607312,0.00005735823,0.00002876593,0.01711172],"genre_scores_gemma":[0.9712064,0.0000114082,0.0001479341,0.001048827,0.00007942088,0.00005483327,0.000003958695,0.00001172248,0.02743552],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6291322,"threshold_uncertainty_score":0.9984364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.671950669471958,"score_gpt":0.4747467497226179,"score_spread":0.1972039197493402,"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."}}