{"id":"W4394685747","doi":"10.2196/51171","title":"Scalable Approach to Consumer Wearable Postmarket Surveillance: Development and Validation Study","year":2024,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"ECG Monitoring and Analysis","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; Computer science; Classifier (UML); Heuristics; Wearable technology; Health care; Scalability; Artificial intelligence; Medicine; Machine learning; Database","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":[0.001026571,0.0001256233,0.0002710969,0.0001749199,0.00008142714,0.0001013451,0.00007188201,0.0000966946,0.00008150787],"category_scores_gemma":[0.0001501232,0.00009200445,0.00003210916,0.0004268154,0.00003794469,0.0001150224,0.00007571316,0.0002562442,0.0001606627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005577468,"about_ca_system_score_gemma":0.0002130226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001243302,"about_ca_topic_score_gemma":0.000001471842,"domain_scores_codex":[0.9983916,0.00003146066,0.0004720023,0.000127299,0.0007604381,0.0002171424],"domain_scores_gemma":[0.9992527,0.00008641299,0.0000321201,0.0001742916,0.00007270562,0.0003818094],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001731441,0.001959114,0.3607046,0.004699097,0.001383298,0.0001051694,0.0555082,0.00003134289,0.00005665366,0.0001127438,0.04305504,0.5322116],"study_design_scores_gemma":[0.007532573,0.001630457,0.1802533,0.00462189,0.0007183795,0.0006055036,0.06462459,0.2620387,0.002098414,0.00005556459,0.4739533,0.001867294],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862776,0.0002654879,0.001595867,0.0003308747,0.0001524311,0.000480145,0.000001994138,0.0001450103,0.01075055],"genre_scores_gemma":[0.9892282,0.00005938217,0.007934519,0.0002875558,0.0001247494,0.000093765,0.00003335172,0.00001381036,0.002224619],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5303443,"threshold_uncertainty_score":0.3751833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02131183582894062,"score_gpt":0.3106476637259067,"score_spread":0.289335827896966,"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."}}