{"id":"W2038476729","doi":"10.1046/j.1460-9592.2003.00021.x","title":"Automatic Sensor Algorithms Expedite Pacemaker Follow‐ups","year":2003,"lang":"en","type":"article","venue":"Pacing and Clinical Electrophysiology","topic":"Cardiac pacing and defibrillation studies","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Grey Nuns Community Hospital","funders":"","keywords":"Medicine; Algorithm; Internal medicine; Computer science","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":[],"consensus_categories":[],"category_scores_codex":[0.0007668301,0.0003586385,0.0004098864,0.0003514798,0.0001045145,0.0004129242,0.0003225487,0.000263355,0.001341561],"category_scores_gemma":[0.005764771,0.0001304608,0.0002396092,0.0002382392,0.0001238709,0.0003104003,0.0003357425,0.0002368169,0.0002731216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001013182,"about_ca_system_score_gemma":0.0002123615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001700651,"about_ca_topic_score_gemma":0.0003312786,"domain_scores_codex":[0.9992952,0.0003046168,0.00008692647,0.00008780137,0.0001845513,0.00004081169],"domain_scores_gemma":[0.9977438,0.001051763,0.0007810633,0.0001922037,0.0001613847,0.00006967386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.007569318,0.001463624,0.2225021,0.0003293749,0.000260219,0.0003388699,0.0001807761,0.006433025,0.02600207,0.000400389,0.003398738,0.7311215],"study_design_scores_gemma":[0.003216437,0.02524906,0.7955666,0.0002160821,0.0007897675,0.008683825,0.0002448228,0.1020901,0.05015488,0.00219512,0.0114528,0.0001406136],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9727897,0.001188911,0.02348966,0.0001113194,0.00005232024,0.000115189,0.0001482395,0.0005276956,0.001577068],"genre_scores_gemma":[0.9845417,0.0002200467,0.01451291,0.00008954226,0.0000761631,0.00008033968,0.0001695392,0.00003433329,0.0002754193],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001341561,"threshold_uncertainty_score":0.004487932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03331679929119277,"score_gpt":0.3537898156231249,"score_spread":0.3204730163319321,"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."}}