{"id":"W4398781413","doi":"10.1017/cjn.2024.150","title":"P.043 Developing a brief clinical dataset for Duchenne Muscular Dystrophy","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Muscle Physiology and Disorders","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services","funders":"","keywords":"Duchenne muscular dystrophy; International Classification of Functioning, Disability and Health; Multidisciplinary approach; Medicine; Guideline; Best practice; Stakeholder; MEDLINE; Delphi method; Family medicine; Physical therapy; Rehabilitation; Computer science; Political science; Pathology; Artificial intelligence; Public relations","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.03662098,0.0007291138,0.00124231,0.01236725,0.002655688,0.00342861,0.002766024,0.001347953,0.01804703],"category_scores_gemma":[0.1138253,0.0007016035,0.002834206,0.008425485,0.0006817517,0.001981761,0.003938089,0.002191258,0.005309946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01168545,"about_ca_system_score_gemma":0.05996479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.11437,"about_ca_topic_score_gemma":0.2352949,"domain_scores_codex":[0.9797106,0.007285941,0.005870742,0.001390339,0.00500716,0.0007352104],"domain_scores_gemma":[0.8752748,0.03784389,0.01066412,0.008702798,0.06342306,0.004091219],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004912133,0.0002941671,0.05013864,0.01294525,0.0005228841,0.000773576,0.002818735,0.003117071,0.001558224,0.0100304,0.6646091,0.2527007],"study_design_scores_gemma":[0.000803464,0.0003707185,0.1355687,0.01606829,0.000662899,0.000824465,0.00329625,0.004359527,0.002180184,0.01154307,0.8240771,0.0002452165],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.02784906,0.004394883,0.09471449,0.02664422,0.00118607,0.04881012,0.7525916,0.003134224,0.04067537],"genre_scores_gemma":[0.06013839,0.002785832,0.3339599,0.005935472,0.0003470926,0.07456867,0.5155157,0.000493979,0.006254949],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.11437,"threshold_uncertainty_score":0.2274086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04634124289137739,"score_gpt":0.3268066438299569,"score_spread":0.2804654009385795,"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."}}