{"id":"W4232516849","doi":"10.33015/dominican.edu/2018.ot.06","title":"Managing Fatigue with Technology for Individuals with Multiple Sclerosis","year":2018,"lang":"en","type":"dissertation","venue":"","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"mHealth; Multiple sclerosis; Conjunction (astronomy); Mobile apps; Psychology; Medicine; Physical medicine and rehabilitation; Computer science; Data science; World Wide Web; Nursing; Psychiatry; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007138014,0.000202931,0.0002068881,0.0002889512,0.000519241,0.0004778541,0.0002083735,0.0004962079,0.004416906],"category_scores_gemma":[0.002697611,0.0000697677,0.0002819674,0.0001920868,0.0001707021,0.0003942981,0.000535154,0.0003209748,0.0006059671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001663191,"about_ca_system_score_gemma":0.0006412838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004734861,"about_ca_topic_score_gemma":0.001397035,"domain_scores_codex":[0.9996932,0.0001092778,0.00002960573,0.00002822596,0.00009021624,0.00004957687],"domain_scores_gemma":[0.9993057,0.0003045062,0.0001799174,0.00002128326,0.0001015032,0.00008707573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0007679028,0.007533195,0.1491517,0.002988072,0.0001677861,0.0003603049,0.01313864,0.0003384479,0.0135673,0.0001892927,0.004025257,0.8077721],"study_design_scores_gemma":[0.0006172336,0.04483934,0.8910303,0.002168164,0.0004810674,0.002916047,0.01640188,0.0006515556,0.009098892,0.0006113629,0.03112743,0.00005672191],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876173,0.003524248,0.0005281483,0.001130634,0.00003699078,0.0002296795,0.00005801921,0.00002695505,0.006848087],"genre_scores_gemma":[0.9889451,0.003800958,0.003482863,0.0004820254,0.00006422758,0.000358645,0.00008264331,0.000003334917,0.002780271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004416906,"threshold_uncertainty_score":0.01477605,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07542866363427979,"score_gpt":0.3392764752852326,"score_spread":0.2638478116509528,"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."}}