{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005429704,0.0001549931,0.0004623924,0.000182187,0.00004498831,0.000009819403,0.0001425273,0.00008688147,0.000007288594],"category_scores_gemma":[0.01219604,0.0001373877,0.000006705839,0.0002956626,0.0001540453,0.00003691409,0.0001132804,0.0002138975,0.000001417032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005682944,"about_ca_system_score_gemma":0.00002681041,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002578081,"about_ca_topic_score_gemma":0.0002561077,"domain_scores_codex":[0.9982457,0.0001397888,0.0005649711,0.0004872212,0.0002007151,0.000361603],"domain_scores_gemma":[0.9927882,0.006465942,0.0001129837,0.0004182587,0.00008738408,0.0001272498],"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":[0.00009686784,0.0002218389,0.005303003,0.0005238875,0.00001040308,0.0001157983,0.0008200115,0.000006453326,0.0006927773,0.8167497,0.0007548166,0.1747044],"study_design_scores_gemma":[0.0007695173,0.0001482584,0.01888778,0.0001174624,0.00002673356,0.00007352378,0.0003244423,0.1597027,0.00003555045,0.819668,0.00008006563,0.0001659707],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006121891,0.00002771831,0.9915502,0.0001324816,0.0001123104,0.0008316587,0.0003027885,0.00001544069,0.0009055294],"genre_scores_gemma":[0.1084652,0.000003327132,0.8911464,0.00007053323,0.0001593802,0.00003601504,0.00009757347,0.00001681449,0.000004827291],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1745384,"threshold_uncertainty_score":0.9961247,"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."}}