{"id":"W4396511904","doi":"10.23889/ijpds.v9i1.2358","title":"Trend control charts for multiple sclerosis case definitions","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia; University of Manitoba","funders":"","keywords":"Multiple sclerosis; Control (management); Computer science; Data science; Natural language processing; Psychology; Artificial intelligence; Psychiatry","routes":{"ca_aff":true,"ca_fund":false,"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":[0.04405642,0.001324183,0.001445844,0.0156162,0.0009271327,0.002807182,0.003055911,0.0008923477,0.01321882],"category_scores_gemma":[0.1871407,0.0007697689,0.001917302,0.01179185,0.0007921673,0.002088893,0.001688365,0.002578036,0.001678136],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002478026,"about_ca_system_score_gemma":0.005400416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02040772,"about_ca_topic_score_gemma":0.01359967,"domain_scores_codex":[0.9583776,0.01882643,0.006547169,0.004086165,0.01150002,0.0006626358],"domain_scores_gemma":[0.8316247,0.07447851,0.03813929,0.01239864,0.04248183,0.0008771204],"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":[0.003318006,0.0006522367,0.1966848,0.004494989,0.001555857,0.0007149816,0.00392517,0.04089874,0.002584798,0.0366534,0.1829502,0.5255668],"study_design_scores_gemma":[0.001963422,0.001990448,0.3595356,0.004906725,0.001396588,0.002399708,0.002298277,0.2356637,0.01065475,0.02467525,0.353612,0.0009034996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.114972,0.00736361,0.7112612,0.001873161,0.002276429,0.0175887,0.08690029,0.02386294,0.03390169],"genre_scores_gemma":[0.3931461,0.00314834,0.5054261,0.0004293758,0.0004384227,0.02865113,0.06133122,0.001844993,0.005584364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04405642,"threshold_uncertainty_score":0.2329954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2095129616871721,"score_gpt":0.4038379860269997,"score_spread":0.1943250243398277,"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."}}