{"id":"W4415039941","doi":"10.1136/bmjhci-2024-101418","title":"Machine learning model to classify chronic leg wounds and identify pyoderma gangrenosum","year":2025,"lang":"en","type":"article","venue":"BMJ Health & Care Informatics","topic":"Wound Healing and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Bundesministerium für Bildung und Forschung","keywords":"Pyoderma gangrenosum; Leg ulcer; Foot (prosody); Wound care; Clinical Practice; Chronic wound","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":[0.000983358,0.0007318931,0.0004328557,0.0005941576,0.000267362,0.0007368248,0.0007307746,0.0008431504,0.001981571],"category_scores_gemma":[0.00189603,0.0001726884,0.0005603777,0.0002931673,0.0001952051,0.0004078422,0.0004029123,0.0008862828,0.0006618895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008450622,"about_ca_system_score_gemma":0.0007873113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008496138,"about_ca_topic_score_gemma":0.005399113,"domain_scores_codex":[0.9997606,0.00004631324,0.00002046573,0.00007241794,0.00004875697,0.00005146414],"domain_scores_gemma":[0.9994078,0.0002757224,0.00005539697,0.00003511725,0.0001974629,0.00002854484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000700833,0.001078711,0.05940395,0.0001958362,0.000278661,0.0003813402,0.0001428469,0.6224461,0.008672693,0.0007789626,0.007206219,0.2987138],"study_design_scores_gemma":[0.00001256394,0.00008485439,0.003400732,0.00001975539,0.00001778098,0.00004720431,0.00001729702,0.9943349,0.001326094,0.0003488749,0.0003837672,0.000006217437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.873603,0.00158553,0.1141388,0.0008981219,0.0002529102,0.0003108424,0.001187737,0.001200201,0.006822837],"genre_scores_gemma":[0.9749591,0.0001709263,0.01997719,0.0001793901,0.00004174584,0.0001286796,0.001073381,0.00002236238,0.003447238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008496138,"threshold_uncertainty_score":0.01689339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03100917085358471,"score_gpt":0.4048254585606502,"score_spread":0.3738162877070654,"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."}}