{"id":"W4405961127","doi":"10.1093/geroni/igae098.1412","title":"STRENGTHS AND LIMITATIONS OF USING DIFFERENT SENSOR TECHNOLOGIES TO ASSESS CLINICAL OUTCOMES IN DYADIC CARE","year":2024,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Telemedicine and Telehealth Implementation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; Bruyère","funders":"","keywords":"Psychology; Computer science","routes":{"ca_aff":true,"ca_fund":false,"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":[0.1661852,0.002275191,0.003048719,0.006083669,0.003049604,0.005995028,0.006247065,0.002272691,0.00210084],"category_scores_gemma":[0.2607153,0.001342554,0.004019537,0.009000485,0.002907067,0.004280478,0.006193653,0.002718815,0.0007192697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002910174,"about_ca_system_score_gemma":0.004744367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01495388,"about_ca_topic_score_gemma":0.02990421,"domain_scores_codex":[0.8219138,0.1099353,0.02749803,0.01087077,0.02752922,0.002253004],"domain_scores_gemma":[0.6808692,0.1736593,0.03068398,0.03591396,0.07523059,0.003642866],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001616954,0.0005946126,0.7527445,0.008789842,0.006381228,0.0003431699,0.008583303,0.003259486,0.0009892661,0.003332726,0.006469622,0.2068952],"study_design_scores_gemma":[0.0006457219,0.005063071,0.8022957,0.02157669,0.007506624,0.002823391,0.0227177,0.02318236,0.004465339,0.02529133,0.08347489,0.0009571397],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.708584,0.06181553,0.112674,0.01933049,0.008533079,0.0170813,0.01519328,0.000390943,0.05639744],"genre_scores_gemma":[0.8396977,0.00801268,0.1117235,0.004567157,0.001569395,0.02988018,0.002574825,0.0001273389,0.001847291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1661852,"threshold_uncertainty_score":0.878882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2045547498384478,"score_gpt":0.4879751065434848,"score_spread":0.283420356705037,"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."}}