{"id":"W2090906954","doi":"10.1002/sim.2791","title":"A likelihood approach to estimating sensitivity and specificity for binocular data: application in ophthalmology","year":2007,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Alexandra Hospital; University of Alberta; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Sensitivity (control systems); Extension (predicate logic); Maximum likelihood; Computer science; Statistics; Binary data; Optometry; Mathematics; Artificial intelligence; Binary number; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03038104,0.001443032,0.002270607,0.00493721,0.0008915417,0.00248159,0.002724488,0.003171058,0.002312196],"category_scores_gemma":[0.186078,0.001026349,0.001775337,0.004081405,0.003584859,0.002748648,0.003122682,0.003971617,0.0007393524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001333046,"about_ca_system_score_gemma":0.001904014,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002806299,"about_ca_topic_score_gemma":0.002355695,"domain_scores_codex":[0.9770386,0.01954951,0.000594205,0.001074591,0.001560195,0.0001829039],"domain_scores_gemma":[0.8379876,0.1528178,0.003288514,0.003286108,0.002141108,0.000478901],"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":[0.0002920967,0.0001735753,0.01765618,0.0007050774,0.0007358354,0.001215877,0.001008631,0.3037076,0.002217782,0.3546186,0.00506342,0.3126054],"study_design_scores_gemma":[0.00005727834,0.0001196133,0.003444795,0.0001454304,0.00007847431,0.001157716,0.0001573934,0.6095952,0.0007161094,0.3808684,0.003542688,0.0001169538],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001911525,0.0004970583,0.9966125,0.0004190576,0.00002244469,0.0000311205,0.0000415407,0.00007350738,0.000391194],"genre_scores_gemma":[0.1148803,0.001561257,0.8811218,0.0002758903,0.0003158546,0.0004005864,0.0002069849,0.0001406539,0.001096698],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03038104,"threshold_uncertainty_score":0.1606722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1162652923719287,"score_gpt":0.4428071937723671,"score_spread":0.3265419014004384,"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."}}