{"id":"W3176188548","doi":"10.2196/28345","title":"Automated Size Recognition in Pediatric Emergencies Using Machine Learning and Augmented Reality: Within-Group Comparative Study","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Pharmaceutical studies and practices","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ruler; Test (biology); Computer science; Service (business); Sample size determination; Artificial intelligence; Sample (material); Simulation; Machine learning; Statistics; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002103599,0.0001429368,0.0003635457,0.0001908267,0.0004307285,0.00005887429,0.00004653494,0.00005417255,0.0002926527],"category_scores_gemma":[0.0007536068,0.0001163755,0.00003239316,0.001352356,0.0001089812,0.000372625,0.0002578329,0.001050729,0.00003318243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001593668,"about_ca_system_score_gemma":0.00008792884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004493763,"about_ca_topic_score_gemma":0.000665552,"domain_scores_codex":[0.9968533,0.001429475,0.0004033871,0.0002696232,0.0006467204,0.000397487],"domain_scores_gemma":[0.9980917,0.001081827,0.0001136729,0.00009094898,0.000471557,0.0001503053],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002901397,0.005187234,0.8752921,0.001245204,0.0006497785,0.0005988797,0.1000561,0.00003623646,0.008194656,0.00004927474,0.0009159169,0.004873124],"study_design_scores_gemma":[0.006632857,0.002720413,0.7619584,0.0002205701,0.0001772762,0.00009187157,0.09543294,0.1281954,0.001807755,0.0001901307,0.002218614,0.0003537849],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9949423,0.001165378,0.000008394568,0.0005242855,0.00005903895,0.0007859264,0.00001418089,0.00006255569,0.002437903],"genre_scores_gemma":[0.9979478,0.001515132,0.0002029547,0.00003701581,0.00005671926,0.00007684553,0.00003546399,0.0000104558,0.0001176205],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1281592,"threshold_uncertainty_score":0.4745655,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3125972198436863,"score_gpt":0.5362065803274105,"score_spread":0.2236093604837242,"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."}}