{"id":"W2088893370","doi":"10.2202/1557-4679.1125","title":"Fitting Smooth-in-Time Prognostic Risk Functions via Logistic Regression","year":2009,"lang":"en","type":"article","venue":"The International Journal of Biostatistics","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Logistic regression; Hazard; Proportional hazards model; Statistics; Parametric statistics; Econometrics; Regression; Hazard ratio; Regression analysis; Mathematics; Computer science; Confidence interval","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"],"consensus_categories":[],"category_scores_codex":[0.009185921,0.0001383772,0.0004669719,0.000370659,0.0001285077,0.0001228282,0.0006473076,0.00007358273,0.0002615251],"category_scores_gemma":[0.01183878,0.000120129,0.000104695,0.0001438775,0.0000698793,0.0002355596,0.0000410411,0.0003607134,0.0006155614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004453208,"about_ca_system_score_gemma":0.0001180903,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001271162,"about_ca_topic_score_gemma":0.00002258741,"domain_scores_codex":[0.995966,0.0002425899,0.00312387,0.0001846336,0.0002638829,0.0002189813],"domain_scores_gemma":[0.9932768,0.002382307,0.003775573,0.000203377,0.000266767,0.000095156],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007129207,0.001721489,0.5830998,0.0001383319,0.0008661448,0.0002730819,0.006285892,0.01391664,0.0003122705,0.1190005,0.2339682,0.03970473],"study_design_scores_gemma":[0.002661167,0.0006769255,0.5769267,0.0007465013,0.00007151713,0.0003075027,0.0007679593,0.05578295,0.00003110553,0.3372341,0.02422918,0.0005644097],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5807262,0.0028793,0.2905673,0.116115,0.005082309,0.0007784896,0.001260456,0.00005113184,0.002539803],"genre_scores_gemma":[0.9873522,0.0001957461,0.007481106,0.003764293,0.0007765052,0.000003561615,0.00002452673,0.00001528967,0.0003867744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.406626,"threshold_uncertainty_score":0.9964849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1943697366688368,"score_gpt":0.4158548233436729,"score_spread":0.221485086674836,"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."}}