{"id":"W7133042157","doi":"","title":"Development of a Prediction Model for Days at Home after Surgery in Patients Undergoing Elective Gastrointestinal Cancer Surgery","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Enhanced Recovery After Surgery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Health Services and Policy Research; Canadian Institute for Health Information","funders":"Ontario Ministry of Health and Long-Term Care","keywords":"Gastrointestinal cancer; Measure (data warehouse); Regression analysis; Quality (philosophy); Quality of life (healthcare); Predictive value of tests; Elective surgery; Predictive modelling; MEDLINE; Cancer recurrence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.005308329,0.0007843075,0.0008006663,0.001270741,0.000505066,0.001424944,0.00111796,0.0008537858,0.002358583],"category_scores_gemma":[0.01131762,0.000344081,0.001198797,0.0008847602,0.0002720934,0.0004973107,0.000867406,0.001597311,0.0006461026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001955558,"about_ca_system_score_gemma":0.003745266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05086137,"about_ca_topic_score_gemma":0.03605156,"domain_scores_codex":[0.999027,0.000437168,0.00007405787,0.0002046276,0.0001391111,0.0001180507],"domain_scores_gemma":[0.9957592,0.002753457,0.0003476889,0.000109698,0.0008015679,0.0002284002],"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":[0.0004514907,0.001007469,0.844889,0.0001016093,0.0003174268,0.0002127006,0.0005287552,0.07899558,0.00037503,0.001476066,0.00764205,0.06400282],"study_design_scores_gemma":[0.0001097574,0.0004698824,0.1122362,0.00009235326,0.000179317,0.00008284108,0.0004436137,0.8830205,0.0003325807,0.001474989,0.001517514,0.00004052957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9281806,0.0002813617,0.0608382,0.003142728,0.0001431042,0.00069981,0.003732829,0.0004060324,0.002575352],"genre_scores_gemma":[0.9621698,0.0001472218,0.03136497,0.0001590445,0.00004854847,0.0006414519,0.003789214,0.00002894439,0.001650881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05086137,"threshold_uncertainty_score":0.1011306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02273569177583151,"score_gpt":0.2986753856691907,"score_spread":0.2759396938933592,"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."}}