{"id":"W2124106073","doi":"10.1109/tbme.2013.2246160","title":"Multiparameter Respiratory Rate Estimation From the Photoplethysmogram","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":484,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research","keywords":"Photoplethysmogram; Respiratory rate; Pulse oximetry; Computer science; Mean squared error; Respiratory system; Pattern recognition (psychology); Speech recognition; Respiratory frequency; Artificial intelligence; Mathematics; Statistics; Computer vision; Medicine; Heart rate; Tidal volume; Anesthesia; Internal medicine; Blood pressure","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001441081,0.0002634604,0.0001902099,0.0001427129,0.0001012021,0.00009263124,0.0002421372,0.0001669737,0.0002009686],"category_scores_gemma":[0.00003129205,0.0001901026,0.000113549,0.0004025298,0.00006996563,0.0002544218,0.000002020159,0.0005425632,0.0004447866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001251115,"about_ca_system_score_gemma":0.00001251132,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000117884,"about_ca_topic_score_gemma":0.000004551559,"domain_scores_codex":[0.9987628,0.00003071419,0.0003088007,0.0002326908,0.0002920543,0.0003729342],"domain_scores_gemma":[0.9987892,0.0006087107,0.00002127888,0.0003476542,0.00003164028,0.0002015195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000004045329,0.0000457264,0.00001194428,0.0000254804,0.0001019644,0.000006420829,0.0001575857,0.4001589,0.5340465,0.000003007513,0.0006316337,0.06480677],"study_design_scores_gemma":[0.0006440315,0.00007996489,0.0008869105,0.0001505255,0.00004321309,0.000003637585,0.00004505087,0.6185682,0.3730856,0.0000683197,0.005929267,0.0004952392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.171004,0.00007419982,0.8258109,0.000096197,0.001921543,0.000322791,0.00002853533,0.0007093392,0.0000324493],"genre_scores_gemma":[0.9921982,0.00002221841,0.006996513,0.0001324023,0.00023646,0.000316736,0.00000652726,0.00007377953,0.00001716844],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8211942,"threshold_uncertainty_score":0.7752159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137129007056849,"score_gpt":0.2090263575971772,"score_spread":0.1976550675266087,"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."}}