{"id":"W3127726773","doi":"10.2196/25704","title":"Using Machine Learning Technologies in Pressure Injury Management: Systematic Review","year":2021,"lang":"en","type":"review","venue":"JMIR Medical Informatics","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; Project 211; National Natural Science Foundation of China","keywords":"CINAHL; Cochrane Library; MEDLINE; Data extraction; Systematic review; Medicine; Critical appraisal; Grey literature; Strengths and weaknesses; Computer science; Artificial intelligence; Meta-analysis; Nursing; Psychological intervention; Psychology; Pathology; Alternative medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01156872,0.001419516,0.006788051,0.0100406,0.0006499977,0.0027864,0.002101816,0.002142522,0.00417798],"category_scores_gemma":[0.05471238,0.0008500585,0.006192447,0.009800114,0.0008476921,0.003253297,0.001364431,0.001211159,0.0003502851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003846824,"about_ca_system_score_gemma":0.01331673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006517279,"about_ca_topic_score_gemma":0.01722641,"domain_scores_codex":[0.9901579,0.003761628,0.003295925,0.0005360302,0.002042215,0.0002062503],"domain_scores_gemma":[0.9481626,0.04167363,0.006242241,0.0004886262,0.003149546,0.0002832424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001135661,0.00002452769,0.0007411656,0.9338619,0.004921014,0.00006491772,0.0001339343,0.0001684872,0.00006184928,0.0001720668,0.001262942,0.05847364],"study_design_scores_gemma":[0.0001492382,0.0001723538,0.002503178,0.9545126,0.03023308,0.0002182999,0.0001768604,0.0002679895,0.0001270098,0.0003144718,0.01129124,0.00003373766],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0006951686,0.9978114,0.0002674142,0.0003047664,0.00009002574,0.0003856203,0.0002020989,0.00001102081,0.0002325585],"genre_scores_gemma":[0.01084804,0.986421,0.001165684,0.0004486327,0.00008915292,0.0008049312,0.0001285739,0.000004314385,0.00008971045],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01156872,"threshold_uncertainty_score":0.06118202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09103174161749274,"score_gpt":0.4825469664362703,"score_spread":0.3915152248187775,"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."}}