{"id":"W2001110565","doi":"10.1177/1352458514538110","title":"Quality control for retinal OCT in multiple sclerosis: validation of the OSCAR-IB criteria","year":2014,"lang":"en","type":"article","venue":"Multiple Sclerosis Journal","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":305,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Institute of Neurological Disorders and Stroke; European Committee for Treatment and Research in Multiple Sclerosis","keywords":"Kappa; Optical coherence tomography; Clinically isolated syndrome; Medicine; Medical physics; Test (biology); Retinal; Cohen's kappa; Computer science; Artificial intelligence; Optometry; Multiple sclerosis; Ophthalmology; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"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","metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.005928964,0.000451311,0.001197319,0.00036555,0.0006738749,0.0001516768,0.0006324373,0.0002418677,0.0001192569],"category_scores_gemma":[0.01831509,0.000323398,0.0007753882,0.000564751,0.000474103,0.0003389008,0.0002033694,0.0008862515,0.00001257309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003730474,"about_ca_system_score_gemma":0.0001967561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002817874,"about_ca_topic_score_gemma":0.0004017623,"domain_scores_codex":[0.9939102,0.001334554,0.001780069,0.0006015758,0.001387353,0.0009862275],"domain_scores_gemma":[0.9939299,0.002789813,0.0008867559,0.0008544818,0.001159145,0.0003799277],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002052878,0.0005330602,0.5111188,0.0002454841,0.0001686161,7.007599e-7,0.0006691163,0.0002738373,0.4714285,0.00003858705,0.00201098,0.0114594],"study_design_scores_gemma":[0.02101894,0.0004395498,0.910144,0.001453358,0.0001055653,0.00001651931,0.000411347,0.0118613,0.05324403,0.0001362865,0.0008628365,0.0003063223],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.984802,0.0002691838,0.008092131,0.003382712,0.0008382639,0.002105111,0.0001984023,0.00005537421,0.0002568432],"genre_scores_gemma":[0.9940426,0.0002610301,0.004124875,0.0004184459,0.0007397884,0.0002482959,0.00002029695,0.00007280015,0.00007190254],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4181845,"threshold_uncertainty_score":0.9999218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.176711262562985,"score_gpt":0.3528978875698814,"score_spread":0.1761866250068964,"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."}}