{"id":"W4390976683","doi":"10.6000/1929-6029.2024.13.02","title":"Analysis of Wide Modified Rankin Score Dataset using Markov Chain Monte Carlo Simulation","year":2024,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Intracerebral and Subarachnoid Hemorrhage Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Markov chain Monte Carlo; Computer science; Logistic regression; Covariate; Monte Carlo method; Markov chain; Machine learning; Bayesian probability; Artificial intelligence; Data mining; Statistics; Econometrics; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005204596,0.000557967,0.0009966813,0.001408273,0.0006659784,0.001346145,0.001610311,0.001249133,0.002796363],"category_scores_gemma":[0.01750839,0.0002874835,0.001154192,0.001214001,0.0007840527,0.0008678995,0.0007555665,0.001497218,0.0002881149],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009952888,"about_ca_system_score_gemma":0.001255397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01849567,"about_ca_topic_score_gemma":0.01973275,"domain_scores_codex":[0.9986669,0.0006723431,0.00007571777,0.0002668093,0.0001758563,0.0001424666],"domain_scores_gemma":[0.9772296,0.01937139,0.0009995106,0.001022163,0.001062714,0.0003146591],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002158835,0.0001459511,0.02535173,0.0001726594,0.0001442773,0.0003821178,0.0001362333,0.9451333,0.0004939248,0.01371604,0.003325319,0.01078258],"study_design_scores_gemma":[0.00001116367,0.00002141159,0.002038393,0.00001417693,0.00001056001,0.00003659029,0.00003040845,0.992664,0.0001770665,0.004531459,0.0004533346,0.00001151406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7439268,0.001226926,0.2408801,0.001506115,0.0001448507,0.0003423782,0.007423026,0.001129603,0.003420302],"genre_scores_gemma":[0.9278446,0.0002668923,0.06303995,0.0001442056,0.0000482873,0.0002934223,0.006877862,0.00008707037,0.001397693],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01849567,"threshold_uncertainty_score":0.03677607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1080600650251058,"score_gpt":0.4874183684286137,"score_spread":0.3793583034035079,"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."}}