{"id":"W2799403901","doi":"10.1001/jamaophthalmol.2018.0987","title":"Improving Reporting Quality in Ophthalmologic Observational Studies That Use Big Data","year":2018,"lang":"en","type":"article","venue":"JAMA Ophthalmology","topic":"Meta-analysis and systematic reviews","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Observational study; Medicine; Quality (philosophy); Optometry; MEDLINE; Data science; Ophthalmology; Medical physics; Intensive care medicine; Pathology; Computer science","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","insufficient_payload"],"consensus_categories":["metaresearch","insufficient_payload"],"category_scores_codex":[0.2398302,0.0004120347,0.00447617,0.0005181967,0.0002671076,0.0008155817,0.003868561,0.0002788519,0.003264566],"category_scores_gemma":[0.5373685,0.000222689,0.0006526686,0.00175315,0.0004216532,0.001194808,0.002082869,0.000362202,0.001053052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008754442,"about_ca_system_score_gemma":0.0001779123,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004064204,"about_ca_topic_score_gemma":0.00009472165,"domain_scores_codex":[0.9565033,0.0163905,0.01995672,0.002722824,0.003768862,0.0006577861],"domain_scores_gemma":[0.8883541,0.03969796,0.04710786,0.01954042,0.004992219,0.0003074058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002248702,0.00007160501,0.9802876,0.00004458948,0.0001521699,0.0006673859,0.000296739,0.000009932473,0.0002296923,0.0007920281,0.006737759,0.010688],"study_design_scores_gemma":[0.0002537928,0.000100996,0.9758709,0.0000353132,0.00006398078,0.002543479,0.001360003,0.002504811,0.00003602623,0.01258049,0.004364425,0.0002857759],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900578,0.0007239677,0.0008471424,0.001518241,0.001632042,0.0005296997,0.00004864349,0.00001033317,0.004632153],"genre_scores_gemma":[0.9812497,0.00000744763,0.01169739,0.0004305921,0.0005642322,0.00004466495,0.00006165173,0.00001595743,0.005928332],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2975383,"threshold_uncertainty_score":0.9997247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9804200485420731,"score_gpt":0.654035555363742,"score_spread":0.3263844931783311,"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."}}