{"id":"W7039443431","doi":"","title":"Mitigating calibration bias without known attribute grouping for improved fairness on predicting future lesional activity in multiple sclerosis","year":2025,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Progressive MS Alliance; McGill University; F. Hoffmann-La Roche; Teva Pharmaceutical Industries; Biogen","keywords":"Calibration; Multiple sclerosis; Pattern recognition (psychology); Feature (linguistics); Artificial neural network","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03605615,0.0005790154,0.001695635,0.0007642057,0.001479812,0.002574114,0.001709961,0.001408757,0.002937776],"category_scores_gemma":[0.127612,0.0004981203,0.001144904,0.001311469,0.001204879,0.002556906,0.002393879,0.003250277,0.000769645],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009790923,"about_ca_system_score_gemma":0.001654078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004820952,"about_ca_topic_score_gemma":0.004630235,"domain_scores_codex":[0.9879287,0.00868591,0.0003608188,0.001657428,0.0009112541,0.000455865],"domain_scores_gemma":[0.8854655,0.09185269,0.003270949,0.01163294,0.006422682,0.001355272],"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":[0.005343677,0.001653825,0.229718,0.0002210614,0.001294625,0.00009588792,0.002657574,0.05110706,0.003754528,0.0123485,0.01454359,0.6772616],"study_design_scores_gemma":[0.0003368937,0.0008260034,0.1139012,0.0003258356,0.001327658,0.0001408518,0.001279336,0.8060905,0.008766444,0.06208928,0.004710986,0.0002049527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.758718,0.001191177,0.2297219,0.003096697,0.0006548449,0.0003047861,0.0004818776,0.0006593512,0.005171377],"genre_scores_gemma":[0.9050456,0.000206527,0.09178862,0.0005004353,0.0002527272,0.0001263233,0.0003648206,0.0001009175,0.001613984],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.03605615,"threshold_uncertainty_score":0.1906854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0951158351828064,"score_gpt":0.310725952694187,"score_spread":0.2156101175113806,"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."}}