{"id":"W4294958917","doi":"10.1136/bmjopen-2021-055346","title":"Development and training of a machine learning algorithm to identify patients at risk for recurrence following an arthroscopic Bankart repair (CLEARER): protocol for a retrospective, multicentre, cohort study","year":2022,"lang":"en","type":"article","venue":"BMJ Open","topic":"Shoulder Injury and Treatment","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hand and Upper Limb Clinic","funders":"","keywords":"Medicine; Bankart repair; Retrospective cohort study; Algorithm; Protocol (science); Cohort; Bankart lesion; Cohort study; Machine learning; Surgery; Artificial intelligence; Arthroscopy; Internal medicine; Alternative medicine","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":[0.03724468,0.002083001,0.003126621,0.00154633,0.001959658,0.001426414,0.00229401,0.002414304,0.01564431],"category_scores_gemma":[0.02719995,0.001708432,0.003869713,0.001495417,0.001897685,0.001205811,0.001845129,0.002489757,0.003801761],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002741314,"about_ca_system_score_gemma":0.01162673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002344443,"about_ca_topic_score_gemma":0.002769884,"domain_scores_codex":[0.9882408,0.006869839,0.00206559,0.001060854,0.001039763,0.0007230992],"domain_scores_gemma":[0.9839034,0.003561154,0.002377842,0.003313027,0.005877519,0.000967033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.3110096,0.1193988,0.08259304,0.04099691,0.004372892,0.003213697,0.006695389,0.03473097,0.0201634,0.006851336,0.0577028,0.3122713],"study_design_scores_gemma":[0.2401924,0.2930797,0.2013486,0.01405588,0.0043308,0.001614387,0.004440806,0.02114559,0.01629355,0.006227429,0.1959558,0.001315183],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"protocol","genre_gemma":"protocol","genre_scores_codex":[0.01984871,0.0002972797,0.009761476,0.0001512578,0.0001278818,0.966478,0.00245094,0.00008217448,0.0008022618],"genre_scores_gemma":[0.005586255,0.0001246939,0.006182448,0.0000753099,0.00001840813,0.9870964,0.0006845816,0.000007757422,0.0002240699],"genre_candidate":"protocol","genre_consensus":"protocol","teacher_disagreement_score":0.03724468,"threshold_uncertainty_score":0.1969711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09569097321282795,"score_gpt":0.4568595758230899,"score_spread":0.361168602610262,"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."}}