{"id":"W3139789393","doi":"10.2196/23495","title":"Integrating Physiological Data Artifacts Detection With Clinical Decision Support Systems: Observational Study","year":2021,"lang":"en","type":"article","venue":"JMIR Biomedical Engineering","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Observational study; Decision support system; Computer science; Clinical decision support system; Data science; Medicine; Data mining; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.007622569,0.0004549914,0.0005918953,0.001037659,0.0008787861,0.001023378,0.0006708452,0.0007692167,0.001632382],"category_scores_gemma":[0.02693762,0.0004479158,0.001060811,0.0009464451,0.0008645254,0.001257508,0.001114355,0.001675616,0.0004841208],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001279523,"about_ca_system_score_gemma":0.002409713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006453482,"about_ca_topic_score_gemma":0.00600353,"domain_scores_codex":[0.9949449,0.002201236,0.0007928926,0.0006420841,0.0009927501,0.0004261391],"domain_scores_gemma":[0.9761038,0.01042677,0.004936643,0.002744039,0.003911031,0.001877816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008081374,0.005439623,0.9714121,0.0001828062,0.0002505504,0.0004761984,0.003110655,0.0005415233,0.0003820371,0.0001811368,0.001119405,0.01609585],"study_design_scores_gemma":[0.0003868464,0.01298203,0.9610416,0.0002040564,0.0004412846,0.00112142,0.009485663,0.007698853,0.001360058,0.000381853,0.004782431,0.0001139679],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9982499,0.0001194112,0.0009039941,0.00004766138,0.000007831658,0.0001782037,0.0002186408,0.000009785139,0.0002644426],"genre_scores_gemma":[0.9976295,0.0001335578,0.001279778,0.0000935857,0.00001852339,0.0002291128,0.0004596001,0.000008072219,0.0001483368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007622569,"threshold_uncertainty_score":0.04031247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.225767895017486,"score_gpt":0.4322113997328551,"score_spread":0.2064435047153691,"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."}}