{"id":"W4416280486","doi":"10.2196/preprints.85644","title":"Artificial Intelligence Enhanced Wound Care to Improve Access, Efficacy and Equity in Wound Care for Older Adults in Rural and Remote Regions of Canada   (Preprint)","year":2025,"lang":"","type":"article","venue":"","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wound care; Health care; Consistency (knowledge bases); Equity (law); MEDLINE; Digital health; Multidisciplinary approach","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001804815,0.000175632,0.0001901261,0.0005444721,0.001405305,0.002629759,0.0006090481,0.0004318863,0.01121198],"category_scores_gemma":[0.008867713,0.00005851923,0.0002958871,0.0007118593,0.0006900622,0.0004806936,0.0009698901,0.0005037147,0.0006762652],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01195905,"about_ca_system_score_gemma":0.03020915,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6913589,"about_ca_topic_score_gemma":0.8168126,"domain_scores_codex":[0.9992203,0.0002551922,0.00003228308,0.00004238791,0.000317589,0.0001322864],"domain_scores_gemma":[0.9971049,0.0006651236,0.0001881936,0.00007629684,0.001312779,0.0006527873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002710301,0.0005791608,0.04699014,0.001220552,0.0001197864,0.000246093,0.003550912,0.004458474,0.001143478,0.01242677,0.3677389,0.5612547],"study_design_scores_gemma":[0.0003309724,0.0006927414,0.3114414,0.002552207,0.0003556472,0.0003484745,0.01781042,0.03246527,0.003962198,0.01645202,0.6134679,0.0001208262],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3681636,0.02096269,0.02562876,0.2446414,0.00565042,0.001721623,0.004555464,0.001757017,0.326919],"genre_scores_gemma":[0.8901999,0.01263034,0.02922665,0.009883485,0.0006439135,0.0002898945,0.001346376,0.0001302452,0.05564925],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3086411,"threshold_uncertainty_score":0.6209174,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04002437191027332,"score_gpt":0.4130112974995498,"score_spread":0.3729869255892765,"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."}}