{"id":"W4390110219","doi":"10.4103/ijo.ijo_1219_23","title":"Optimization of biometry for best refractive outcome in cataract surgery","year":2023,"lang":"en","type":"review","venue":"Indian Journal of Ophthalmology","topic":"Ophthalmology and Visual Impairment Studies","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Cataract surgery; Power (physics); Personalization; Cornea; Modalities; Computer science; Intraocular lens power calculation; Refraction; Optometry; Medicine; Ophthalmology; Optics; Keratometer; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001704027,0.0003780305,0.004329879,0.00319057,0.00005100026,0.000005849857,0.0002083201,0.000723768,0.0001084302],"category_scores_gemma":[0.002416709,0.0002947465,0.001038813,0.001111558,0.0002189304,0.0001246748,0.00006452141,0.0007615649,0.00002325307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002111012,"about_ca_system_score_gemma":0.0007581966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001711674,"about_ca_topic_score_gemma":1.975981e-7,"domain_scores_codex":[0.9964641,0.0002703428,0.002376871,0.0002720981,0.0002126263,0.0004039889],"domain_scores_gemma":[0.9936045,0.002958334,0.002720775,0.0002346931,0.0003431272,0.0001385448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"case_report","study_design_scores_codex":[0.003740231,0.009256958,0.4119194,0.2294342,0.01382392,0.1313968,0.001415472,0.0001852898,0.000003297406,0.00005161228,0.002144352,0.1966284],"study_design_scores_gemma":[0.006467925,0.03217725,0.04168316,0.1206573,0.01142024,0.7407145,0.001804979,0.00001844871,0.00001707123,0.0003894069,0.04264032,0.002009332],"study_design_candidate":"case_report","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.03163242,0.9652563,0.00003545673,0.0001405899,0.001638432,0.00101611,0.0001485952,0.000007228629,0.0001248829],"genre_scores_gemma":[0.01318523,0.9846618,0.001039533,0.00001555962,0.0003997533,0.00005307848,0.0001506826,0.00009502294,0.0003993312],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.6093177,"threshold_uncertainty_score":0.9999505,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2532403285948128,"score_gpt":0.495004926502611,"score_spread":0.2417645979077981,"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."}}