{"id":"W4283734438","doi":"10.1001/jamaoto.2022.1629","title":"Development and Validation of a Machine Learning Algorithm Predicting Emergency Department Use and Unplanned Hospitalization in Patients With Head and Neck Cancer","year":2022,"lang":"en","type":"article","venue":"JAMA Otolaryngology–Head & Neck Surgery","topic":"Cancer survivorship and care","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre; Institute for Clinical Evaluative Sciences; Public Health Ontario; Dalhousie University; University of Toronto","funders":"","keywords":"Emergency department; Head and neck cancer; Computer science; Head and neck; Machine learning; Head (geology); Algorithm; Medicine; Cancer; Medical emergency; Artificial intelligence; Surgery; Internal medicine; Nursing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.008671667,0.0007356521,0.0007622264,0.001224976,0.0006988302,0.0009368124,0.001215912,0.001060831,0.0008128908],"category_scores_gemma":[0.01741098,0.0003654205,0.0007381943,0.0006151198,0.000363043,0.0005764081,0.0008083034,0.001181648,0.0002949018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00130356,"about_ca_system_score_gemma":0.003662721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01834772,"about_ca_topic_score_gemma":0.01107633,"domain_scores_codex":[0.9986142,0.0006649422,0.0001621956,0.0002613402,0.0001935734,0.0001037886],"domain_scores_gemma":[0.9920056,0.005171519,0.0004810831,0.0002977306,0.001832805,0.0002112481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006746103,0.0009293135,0.3159005,0.00009420461,0.0004285153,0.0001651187,0.000171106,0.5060949,0.001124996,0.0004680989,0.002267199,0.1716814],"study_design_scores_gemma":[0.00003580405,0.0001311775,0.006827828,0.00001683444,0.00003101881,0.00002684718,0.00002478624,0.9921811,0.0003666222,0.0001897807,0.0001618158,0.000006354788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8568523,0.0006547275,0.1369836,0.001161372,0.0001074174,0.0006677513,0.0006448954,0.0008684158,0.002059438],"genre_scores_gemma":[0.9283044,0.0001306854,0.06978758,0.0001535193,0.00003507795,0.00028868,0.0007816371,0.00001801618,0.0005002828],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01834772,"threshold_uncertainty_score":0.04586071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0142117506928861,"score_gpt":0.2416668710464759,"score_spread":0.2274551203535898,"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."}}