{"id":"W4324332698","doi":"10.1016/j.jcjo.2023.01.016","title":"Training visual pattern recognition in ophthalmology using a perceptual and adaptive learning module","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Ophthalmology","topic":"Ophthalmology and Visual Health Research","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"That Man May See; Research to Prevent Blindness","keywords":"Fluency; Test (biology); Palm; Audiology; Psychology; Perception; Session (web analytics); Perceptual learning; Computer science; Artificial intelligence; Medicine; Mathematics education; Neuroscience","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":[0.0003375858,0.0004282164,0.0003030448,0.0002569954,0.0001883104,0.000456013,0.0008416513,0.0005446863,0.004097545],"category_scores_gemma":[0.001034332,0.0001984381,0.0003722736,0.0002750233,0.0002363225,0.0005732108,0.0004953751,0.0005970841,0.0009305527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000261019,"about_ca_system_score_gemma":0.0005730122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003140938,"about_ca_topic_score_gemma":0.003144753,"domain_scores_codex":[0.9998643,0.00001628054,0.000009070186,0.00005779517,0.0000303544,0.00002215119],"domain_scores_gemma":[0.9995967,0.0001723283,0.00002761368,0.0000533756,0.0001081931,0.0000418644],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003489076,0.0008650589,0.004185732,0.0001087125,0.00007680792,0.00007771287,0.0000900423,0.03723425,0.115761,0.001593878,0.001451,0.8382069],"study_design_scores_gemma":[0.00006042603,0.0008627733,0.007977173,0.00003040415,0.0000912606,0.0002155135,0.00005387483,0.9158121,0.06964266,0.002585185,0.00263612,0.00003259948],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1897842,0.0002822091,0.8013939,0.0001508857,0.0001155484,0.0001958895,0.00007353567,0.002881809,0.005122015],"genre_scores_gemma":[0.7097187,0.0002127338,0.2844198,0.0001903494,0.00003909646,0.0001516575,0.0001550548,0.00006640657,0.00504617],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004097545,"threshold_uncertainty_score":0.0137077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3336186388600358,"score_gpt":0.4690615652585495,"score_spread":0.1354429263985137,"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."}}