{"id":"W4407675203","doi":"10.1097/sla.0000000000006671","title":"Recovery Patterns","year":2025,"lang":"en","type":"article","venue":"Annals of Surgery","topic":"Enhanced Recovery After Surgery","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Medicine; Medoid; Cluster analysis; Concomitant; Cluster (spacecraft); Physical therapy; Surgery; Artificial intelligence; Computer science","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":[0.00132678,0.0003128287,0.0003322893,0.001818223,0.0007293806,0.0009001932,0.0008953153,0.0004349577,0.0165215],"category_scores_gemma":[0.01051355,0.0001255746,0.0006228478,0.002533888,0.0003839848,0.0008992613,0.0014972,0.0006601684,0.004459326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001288609,"about_ca_system_score_gemma":0.00164581,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007491181,"about_ca_topic_score_gemma":0.008484903,"domain_scores_codex":[0.998066,0.0003213817,0.0003451334,0.0004264461,0.0004844791,0.0003565356],"domain_scores_gemma":[0.9950836,0.0009009977,0.001698644,0.0005568842,0.001378733,0.0003810843],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00102578,0.0003022792,0.7116653,0.0007495368,0.0001981599,0.0007662031,0.002865369,0.001845454,0.001367164,0.003951928,0.02168774,0.253575],"study_design_scores_gemma":[0.0000751446,0.0004065332,0.9189805,0.0007567407,0.00009522241,0.002561071,0.006860998,0.004950266,0.001145503,0.0080544,0.05604517,0.00006858548],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8965836,0.001587013,0.01696163,0.005548327,0.0001511263,0.001505731,0.03241103,0.0006575205,0.04459408],"genre_scores_gemma":[0.9738634,0.0006622301,0.005188238,0.0005849256,0.00003460835,0.0005983711,0.01094271,0.00009429234,0.008031231],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0165215,"threshold_uncertainty_score":0.05526996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1459657466265198,"score_gpt":0.3541100763665354,"score_spread":0.2081443297400156,"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."}}