{"id":"W2910439267","doi":"10.1016/j.ogla.2019.01.004","title":"Improving the Feasibility of Glaucoma Clinical Trials Using Trend-Based Visual Field Progression End Points","year":2019,"lang":"en","type":"article","venue":"Ophthalmology Glaucoma","topic":"Glaucoma and retinal disorders","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"Nova Scotia Health Authority; Dalhousie University","funders":"National Eye Institute; National Health and Medical Research Council; National Institutes of Health","keywords":"Medicine; Visual field; Pointwise; Glaucoma; Sample size determination; Clinical trial; Intraocular pressure; Observational study; Ophthalmology; Statistics; Mathematics; Internal medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2336452,0.001625166,0.004194577,0.003058283,0.0009518221,0.005559995,0.001675405,0.004292451,0.009426783],"category_scores_gemma":[0.3677133,0.001086929,0.005053123,0.003502982,0.001299203,0.007979943,0.002978409,0.005247668,0.00167789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001641824,"about_ca_system_score_gemma":0.006502258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00149878,"about_ca_topic_score_gemma":0.001938311,"domain_scores_codex":[0.7447601,0.2232362,0.01370764,0.005187678,0.01086099,0.002247373],"domain_scores_gemma":[0.5931829,0.3268053,0.03042484,0.01514549,0.03054055,0.003900908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.1936263,0.004201906,0.08612221,0.01293301,0.01813674,0.0001952223,0.001141718,0.01269912,0.002956559,0.02288835,0.03622609,0.608873],"study_design_scores_gemma":[0.2076757,0.1385114,0.2124274,0.01243352,0.03174005,0.0006551964,0.001287346,0.1122578,0.008568722,0.1164539,0.1571068,0.0008821778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.3877157,0.07437322,0.3127838,0.08710931,0.01279308,0.04507278,0.01605546,0.002801181,0.06129551],"genre_scores_gemma":[0.8168643,0.006270322,0.1379744,0.009112235,0.002433584,0.02109427,0.003779206,0.0002809678,0.002190819],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2336452,"threshold_uncertainty_score":0.945052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1018113142237039,"score_gpt":0.450691419396237,"score_spread":0.3488801051725331,"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."}}