{"id":"W4401372998","doi":"10.1177/09544119241264304","title":"Optimization of activity-driven event detection for long-term ambulatory urodynamics","year":2024,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part H Journal of Engineering in Medicine","topic":"Urinary Bladder and Prostate Research","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"International Collaboration On Repair Discoveries; University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institutes of Health; U.S. Department of Veterans Affairs","keywords":"Context (archaeology); Inertial measurement unit; Computer science; Artificial intelligence; False positive paradox; Noise (video); Overhead (engineering); Event (particle physics); Real-time computing; Machine learning","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":[0.0005344065,0.0009311212,0.0006400818,0.0006717115,0.000256533,0.0006351831,0.0005824313,0.0004583833,0.001791369],"category_scores_gemma":[0.001880553,0.0002552475,0.0005265599,0.0005380661,0.000190193,0.0004931857,0.000404511,0.0006332422,0.0007804203],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002798111,"about_ca_system_score_gemma":0.0007112951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003237332,"about_ca_topic_score_gemma":0.003315549,"domain_scores_codex":[0.9997073,0.00005032225,0.00002518891,0.00008517383,0.00008353455,0.00004847554],"domain_scores_gemma":[0.9993806,0.0003057063,0.00007451609,0.00003765255,0.000164075,0.00003750229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005403638,0.000505371,0.008769129,0.0001618306,0.0000915487,0.0001404237,0.00007583142,0.1954489,0.05007735,0.0007753294,0.001710129,0.7417037],"study_design_scores_gemma":[0.00001180521,0.0001313776,0.003782958,0.000007684625,0.00001735257,0.00003716969,0.00002492883,0.9881216,0.00693658,0.0003520966,0.0005683035,0.000008081221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09598474,0.00036102,0.8999966,0.0001991213,0.00005462149,0.0001085056,0.0001917164,0.001947254,0.001156411],"genre_scores_gemma":[0.7395519,0.0002606739,0.2571064,0.0001285227,0.00007075127,0.0001903169,0.0006174053,0.0001060428,0.001967977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003237332,"threshold_uncertainty_score":0.006437004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01977076617640271,"score_gpt":0.2946102010348579,"score_spread":0.2748394348584552,"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."}}