{"id":"W2910141964","doi":"10.2147/mder.s186529","title":"Undermining a common language: smartphone applications for eye emergencies","year":2019,"lang":"en","type":"article","venue":"Medical Devices Evidence and Research","topic":"Ophthalmology and Visual Health Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Optometry; Medicine; Medical emergency; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.004446303,0.00009728262,0.0002933694,0.0002149259,0.0003361597,0.00002619321,0.0002385215,0.0002348555,0.001228581],"category_scores_gemma":[0.001275876,0.00007053798,0.00004985198,0.0005310784,0.0004170907,0.0001134024,0.0001969291,0.0008091533,0.0003725142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004299217,"about_ca_system_score_gemma":0.0005915087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003477203,"about_ca_topic_score_gemma":0.0000857277,"domain_scores_codex":[0.9973027,0.0002461445,0.0002778457,0.0003764238,0.001176002,0.0006208759],"domain_scores_gemma":[0.9962477,0.00256275,0.00003134213,0.0003120801,0.0003015479,0.000544569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007806913,0.0004815466,0.8371192,0.008900332,0.00007900296,0.0001450349,0.001768908,2.540141e-7,0.002073289,0.001779749,0.004345834,0.1425262],"study_design_scores_gemma":[0.003937224,0.007372539,0.8313721,0.006146032,0.00008550216,0.0004933076,0.01455541,0.008998672,0.001952011,0.001724497,0.1228251,0.0005375081],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9625619,0.0121428,0.000111718,0.02209793,0.0000790217,0.001726371,0.000003323993,0.00003202204,0.001244931],"genre_scores_gemma":[0.9905264,0.001693556,0.0001808887,0.0007930638,0.000289728,0.0004759151,0.00001589389,0.00001486556,0.006009688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1419887,"threshold_uncertainty_score":0.9996845,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3100867127835325,"score_gpt":0.6257157971289605,"score_spread":0.315629084345428,"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."}}