{"id":"W4410393752","doi":"10.1109/jbhi.2025.3570664","title":"An Inferential Model for Understanding the Effects of Demographic and Gait Factors and Their Interactions on the Human Gait Index: A Beta Regression Approach","year":2025,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Balance, Gait, and Falls Prevention","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gait; Regression; Regression analysis; Index (typography); Physical medicine and rehabilitation; Gait analysis; Computer science; Econometrics; Medicine; Statistics; Machine learning; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02095035,0.002607683,0.002683871,0.002675456,0.0006597156,0.002430754,0.002740406,0.002226445,0.006124624],"category_scores_gemma":[0.04926665,0.001080593,0.003007402,0.002113065,0.001553976,0.00205572,0.001770538,0.004163057,0.001370216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001333042,"about_ca_system_score_gemma":0.002069362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01605221,"about_ca_topic_score_gemma":0.006027154,"domain_scores_codex":[0.9893275,0.007618793,0.0003568761,0.001527504,0.0006857672,0.0004835383],"domain_scores_gemma":[0.9461918,0.04987559,0.001477863,0.001013267,0.001190846,0.0002506019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001289842,0.0007819255,0.06260608,0.0006519831,0.002220354,0.001937306,0.001792696,0.6459881,0.002291339,0.1276185,0.004363918,0.1484579],"study_design_scores_gemma":[0.0000385337,0.0002506911,0.00281116,0.00005178921,0.0001408231,0.0001256612,0.0001120399,0.9713628,0.0001536785,0.02384575,0.001073162,0.00003393398],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06347749,0.0008193855,0.9304054,0.001069626,0.0001488242,0.0003344755,0.001114557,0.0005579749,0.002072213],"genre_scores_gemma":[0.7693833,0.00191145,0.215655,0.0005774843,0.0004011863,0.002546382,0.001951511,0.000156421,0.00741726],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02095035,"threshold_uncertainty_score":0.1107973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08376330392195803,"score_gpt":0.3987546694053234,"score_spread":0.3149913654833654,"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."}}