{"id":"W3023140140","doi":"10.1101/2020.04.30.20086660","title":"Machine Learning for Pattern Detection in Cochlear Implant FDA Adverse Event Reports","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Health Systems, Economic Evaluations, Quality of Life","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"University of Toronto","keywords":"Cochlear implant; Adverse effect; Adverse Event Reporting System; Event (particle physics); Medicine; Artificial intelligence; Database; Machine learning; Computer science; Audiology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.007866626,0.001063253,0.001085049,0.007240274,0.0005455795,0.001708654,0.001353505,0.0009835843,0.001641695],"category_scores_gemma":[0.03129545,0.0002973356,0.001295594,0.003221285,0.0005819918,0.001500763,0.001114485,0.001444827,0.0009500545],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001106296,"about_ca_system_score_gemma":0.001904715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00533392,"about_ca_topic_score_gemma":0.004843283,"domain_scores_codex":[0.9929842,0.003273196,0.001154848,0.001338652,0.000949795,0.0002993326],"domain_scores_gemma":[0.959044,0.03156836,0.003860381,0.002001825,0.003135114,0.0003904236],"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":[0.0008847316,0.001642527,0.2629787,0.0006665185,0.0005640175,0.0005177364,0.0003960305,0.09131013,0.003107562,0.001544766,0.00926345,0.6271238],"study_design_scores_gemma":[0.00005265461,0.0002300802,0.02092308,0.00006684187,0.00005980585,0.0001849522,0.0001795736,0.9714415,0.001952372,0.003852415,0.001029574,0.00002713722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6215929,0.002601854,0.3514014,0.002210806,0.0002748575,0.001397959,0.01103661,0.005532633,0.003951042],"genre_scores_gemma":[0.8648013,0.0003187549,0.126015,0.000164596,0.0001624523,0.0004775569,0.007347239,0.00004569053,0.000667395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007866626,"threshold_uncertainty_score":0.04160315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2317759572893423,"score_gpt":0.3967726272385225,"score_spread":0.1649966699491802,"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."}}