{"id":"W4365139349","doi":"10.1093/pch/pxad013","title":"Remote diagnostic imaging using artificial intelligence for diagnosing hip dysplasia in infants: Results from a mixed-methods feasibility pilot study","year":2023,"lang":"en","type":"article","venue":"Paediatrics & Child Health","topic":"Hip disorders and treatments","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Alberta Health Services","funders":"Canadian Medical Association; Alberta Innovates; Women and Children's Health Research Institute; Children's Health Research Institute; Radiological Society of North America","keywords":"Medicine; Hip dysplasia; Artificial intelligence; Medical physics; Computer science; Radiology; Radiography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.01798394,0.0007530155,0.0007072901,0.0007151868,0.0009221199,0.0009281252,0.0009226302,0.0006813155,0.00139039],"category_scores_gemma":[0.01989288,0.0005430673,0.000814738,0.0003910012,0.0007052258,0.000656959,0.0008150312,0.0005785578,0.0003557159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001283683,"about_ca_system_score_gemma":0.002282333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01143008,"about_ca_topic_score_gemma":0.01518753,"domain_scores_codex":[0.988835,0.008659841,0.0005741034,0.0004303245,0.001073757,0.0004269968],"domain_scores_gemma":[0.985476,0.008325092,0.001101686,0.001014983,0.003309915,0.0007723195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0345923,0.1369389,0.5565317,0.002522656,0.000781382,0.002118402,0.04643061,0.001339168,0.01501104,0.0003601486,0.00095412,0.2024196],"study_design_scores_gemma":[0.007846196,0.4300167,0.5200115,0.0003304396,0.0007277419,0.001482808,0.02365815,0.005070269,0.007703086,0.0002257179,0.002757806,0.0001695566],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.996695,0.00006460941,0.0006941922,0.00003487475,0.000004744832,0.002104194,0.00005188993,0.00001053831,0.0003399703],"genre_scores_gemma":[0.9861413,0.0002691479,0.009220354,0.0001361673,0.00001897746,0.00357166,0.0001314108,0.000008321085,0.0005026857],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01798394,"threshold_uncertainty_score":0.09510922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0873689666820719,"score_gpt":0.4195098745018836,"score_spread":0.3321409078198117,"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."}}