{"id":"W4284966247","doi":"10.2196/39547","title":"Automatically Identifying Twitter Users for Interventions to Support Dementia Family Caregivers: Annotated Data Set and Benchmark Classification Models","year":2022,"lang":"en","type":"article","venue":"JMIR Aging","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine","keywords":"Dementia; Computer science; Psychological intervention; Classifier (UML); Support vector machine; Artificial intelligence; Artificial neural network; Machine learning; Scalability; Recall; Encoder; Set (abstract data type); Natural language processing; Medicine; Psychology; Disease; Database; Psychiatry; Cognitive psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.003138634,0.001574682,0.000686314,0.002650016,0.0009278879,0.001093626,0.001251003,0.001738037,0.001491141],"category_scores_gemma":[0.01057271,0.0002670142,0.0007994293,0.001431225,0.0006772146,0.001528426,0.001374818,0.001237452,0.001814482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001251785,"about_ca_system_score_gemma":0.0008823205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01619438,"about_ca_topic_score_gemma":0.01897643,"domain_scores_codex":[0.998051,0.0006764156,0.0002613177,0.0004935181,0.0003612892,0.0001564243],"domain_scores_gemma":[0.9927244,0.00357456,0.0007118397,0.0008935063,0.001764285,0.0003315306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003915172,0.003560026,0.478926,0.00200174,0.0005702699,0.002881842,0.002741975,0.06877951,0.0138155,0.001438271,0.1049027,0.3164671],"study_design_scores_gemma":[0.000274105,0.001289081,0.1960123,0.0003705059,0.0002619314,0.001068818,0.004550623,0.7450375,0.01701277,0.002790437,0.03114243,0.000189514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9281775,0.001367075,0.02171097,0.001833579,0.0005429272,0.0009027715,0.03902216,0.002434907,0.004007931],"genre_scores_gemma":[0.8466039,0.0004721152,0.0394707,0.0003472008,0.0002234274,0.0008214017,0.1085747,0.00009296855,0.003393574],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01619438,"threshold_uncertainty_score":0.03220022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2030834324741914,"score_gpt":0.4336174403588767,"score_spread":0.2305340078846853,"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."}}