{"id":"W3169092018","doi":"10.1155/2021/6172815","title":"Blood Biomarkers Predict Cardiac Workload Using Machine Learning","year":2021,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Heart Rate Variability and Autonomic Control","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto","funders":"","keywords":"Workload; Biomarker; Linear regression; Blood pressure; Medicine; Heart rate; Regression analysis; Correlation; Internal medicine; Cardiology; Stepwise regression; Statistics; Computer science; Mathematics; Biology","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":[0.001791904,0.0008591191,0.0007046984,0.001531031,0.0001825225,0.000881475,0.000618228,0.0004974795,0.001310709],"category_scores_gemma":[0.005074733,0.0002112153,0.0007391904,0.0009151659,0.0001672131,0.0004966678,0.0003154471,0.0005808785,0.0006646269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003808778,"about_ca_system_score_gemma":0.000528289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003525564,"about_ca_topic_score_gemma":0.002184773,"domain_scores_codex":[0.9994202,0.0002311234,0.00004268085,0.0001333794,0.0001106935,0.00006203788],"domain_scores_gemma":[0.9977901,0.001363071,0.0003493633,0.0001105529,0.0002887366,0.00009827731],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004906738,0.0006032508,0.6281464,0.0001678572,0.00076343,0.0002207384,0.00009717737,0.1788073,0.00318449,0.0006046475,0.003244912,0.1836692],"study_design_scores_gemma":[0.00002076006,0.0002262071,0.08642191,0.00005752637,0.00009480814,0.0001116947,0.00003253571,0.9090679,0.001096684,0.002071843,0.0007718496,0.00002624735],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8431786,0.003174494,0.1469502,0.0009585426,0.0001872697,0.0001416619,0.002126378,0.0007694585,0.002513393],"genre_scores_gemma":[0.9837578,0.0003819626,0.01384206,0.00009141741,0.00009305968,0.00007197021,0.001200823,0.00001228162,0.0005486441],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003525564,"threshold_uncertainty_score":0.009476662,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08207007733085804,"score_gpt":0.3843007377725248,"score_spread":0.3022306604416667,"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."}}