{"id":"W4413274834","doi":"10.1016/j.bspc.2025.108347","title":"Supervised sequential contrastive regression: Improving performance on imbalanced rehabilitation exercises","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Toronto Rehabilitation Institute; University Health Network","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Regression; Rehabilitation; Artificial intelligence; Machine learning; Regression analysis; Linear regression; Pattern recognition (psychology); Physical medicine and rehabilitation; Statistics; Mathematics; Physical therapy; Medicine","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":[0.001202637,0.001218196,0.00109241,0.000695657,0.0003005278,0.0005644186,0.0008067195,0.0008050764,0.002778765],"category_scores_gemma":[0.003393613,0.0002697321,0.0004595746,0.0004773417,0.0002258679,0.0006059016,0.0008717427,0.0009107058,0.001141374],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002044558,"about_ca_system_score_gemma":0.000533145,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002736528,"about_ca_topic_score_gemma":0.004437086,"domain_scores_codex":[0.9995684,0.00009802768,0.00002589326,0.0001599658,0.00009670498,0.00005095595],"domain_scores_gemma":[0.9987532,0.0006930181,0.0000885687,0.0001311391,0.0002574267,0.00007664894],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00258787,0.0009883308,0.005015349,0.0002565838,0.0001885829,0.0001767067,0.0001027582,0.07075623,0.05340848,0.0005678473,0.004790865,0.8611605],"study_design_scores_gemma":[0.00005669587,0.0004851173,0.006045781,0.0000186276,0.00006165005,0.0000857741,0.00002967235,0.9794956,0.01225328,0.000503074,0.0009517015,0.00001297947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4752349,0.001910761,0.5120249,0.0003347209,0.0005253921,0.0002138231,0.0008452468,0.004081887,0.004828251],"genre_scores_gemma":[0.877513,0.0003013396,0.1144733,0.0001372423,0.0001460827,0.0001154668,0.001180152,0.0002455665,0.005887928],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002778765,"threshold_uncertainty_score":0.009295881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008597259272130895,"score_gpt":0.2546839958405389,"score_spread":0.246086736568408,"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."}}