{"id":"W2604148133","doi":"10.1161/circoutcomes.10.suppl_3.150","title":"Abstract 150: Machine Learning Methodology Predicts Comorbidities are Associated With Increased Total Healthcare Costs Among Patients With Severe Peripheral Artery Disease","year":2017,"lang":"en","type":"article","venue":"Circulation Cardiovascular Quality and Outcomes","topic":"Peripheral Artery Disease Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Berger (Canada)","funders":"","keywords":"Medicine; Stroke (engine); Population; Health care; Emergency medicine; Gangrene; Severity of illness; Internal medicine; Surgery","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":[0.004235592,0.0005560769,0.0006299585,0.001200052,0.000277161,0.0008867672,0.0004486629,0.0004615117,0.004071089],"category_scores_gemma":[0.01961938,0.0001834115,0.001027851,0.0007773823,0.0002324436,0.0004763317,0.0005129488,0.000764244,0.0004119647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004150793,"about_ca_system_score_gemma":0.0008829521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002234258,"about_ca_topic_score_gemma":0.001836259,"domain_scores_codex":[0.9983281,0.0009420972,0.0001731486,0.000234943,0.0002200507,0.0001015584],"domain_scores_gemma":[0.9885821,0.008046555,0.001564737,0.0006242586,0.0008014271,0.0003808149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005954392,0.0003064667,0.9713905,0.00004818591,0.0004799936,0.00004254275,0.00002649408,0.01055977,0.0002713017,0.00009327864,0.0008706387,0.01531531],"study_design_scores_gemma":[0.0001195124,0.001073049,0.7406497,0.00007875982,0.0003129695,0.0003406803,0.00009710026,0.2546145,0.00070667,0.001211196,0.0007670411,0.00002876674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9915041,0.0003402237,0.005570377,0.0004158815,0.00003287195,0.00004955697,0.00113475,0.00006929514,0.0008828304],"genre_scores_gemma":[0.9967422,0.00007469874,0.001989147,0.00006214313,0.00003547099,0.00003228331,0.0008301277,0.000006144917,0.0002277721],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004235592,"threshold_uncertainty_score":0.02240026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05864817197847548,"score_gpt":0.3023531166868995,"score_spread":0.243704944708424,"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."}}