{"id":"W4406063502","doi":"10.1016/j.jocd.2024.101560","title":"Development of an Algorithm to Predict Appendicular Lean Mass Index From Regional Spine and Hip Dxa Scans","year":2025,"lang":"en","type":"article","venue":"Journal of Clinical Densitometry","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Orthopaedic Innovation Centre","funders":"","keywords":"Medicine; Index (typography); Lean body mass; SPINE (molecular biology); Algorithm; Internal medicine; Bioinformatics; Computer science; Body weight","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001852208,0.000122732,0.0008158177,0.0006050674,0.00006286368,0.00001384936,0.0001225148,0.0001866855,0.00004886433],"category_scores_gemma":[0.000542257,0.0001014667,0.0001472862,0.0004425361,0.00008809798,0.00007430644,0.00005337692,0.0006319489,0.000004262992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006454743,"about_ca_system_score_gemma":0.0005502618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001100572,"about_ca_topic_score_gemma":0.000005103587,"domain_scores_codex":[0.9970896,0.00008546276,0.001821682,0.0002097769,0.000605588,0.0001878893],"domain_scores_gemma":[0.9980758,0.0002545587,0.000482094,0.0001819918,0.0003989521,0.0006066129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001403517,0.0009208794,0.6274651,0.0003127871,0.0005259559,0.00044594,0.0001619916,0.000005073813,0.001905932,0.00007334574,0.005640141,0.3611393],"study_design_scores_gemma":[0.004673981,0.0005494798,0.9710113,0.001598146,0.0001265893,0.0001224387,0.0003241598,0.0007930617,0.00107317,0.0006950002,0.0189343,0.00009841033],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9645147,0.0005540129,0.03034389,0.003509061,0.0007542892,0.0001637082,0.000005706338,0.00001235672,0.0001423143],"genre_scores_gemma":[0.707896,0.0002059165,0.286866,0.004014181,0.0008897622,0.000001114623,0.00001015967,0.00001192201,0.0001049601],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3610409,"threshold_uncertainty_score":0.413769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07624085649054022,"score_gpt":0.4466993517784202,"score_spread":0.37045849528788,"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."}}