{"id":"W2346796776","doi":"10.1186/s12877-016-0273-7","title":"Understanding the role of contrasting urban contexts in healthy aging: an international cohort study using wearable sensor devices (the CURHA study protocol)","year":2016,"lang":"en","type":"article","venue":"BMC Geriatrics","topic":"Technology Use by Older Adults","field":"Social Sciences","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Université de Montréal","funders":"Direction Générale des Infrastructures, des Transports et de la Mer; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research; Ministère de l'Écologie, du Développement Durable et de l'Énergie","keywords":"Medicine; Wearable computer; Protocol (science); Cohort; Rehabilitation; Cohort study; Wearable technology; Gerontology; Physical medicine and rehabilitation; Physical therapy; Embedded system; Pathology; Alternative medicine; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.010278,0.0008539329,0.0008534653,0.001512481,0.00275779,0.001995993,0.001519897,0.001658594,0.007691755],"category_scores_gemma":[0.007786473,0.001288194,0.001549056,0.0021538,0.001069529,0.002113516,0.002927035,0.001653039,0.002365403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001313813,"about_ca_system_score_gemma":0.005674259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01590412,"about_ca_topic_score_gemma":0.03527111,"domain_scores_codex":[0.9972313,0.001089377,0.0004191123,0.0005706563,0.0002746774,0.0004148225],"domain_scores_gemma":[0.9958166,0.0006150551,0.0009840718,0.001043623,0.001050617,0.0004899947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.008707076,0.003721487,0.8572357,0.005141496,0.0011676,0.001042892,0.03513742,0.0006111922,0.002729507,0.003078787,0.03722945,0.04419757],"study_design_scores_gemma":[0.001410826,0.002955204,0.9342586,0.001446182,0.0006715642,0.0003769833,0.0138152,0.0004989766,0.0006282051,0.0009472809,0.0428481,0.0001428139],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"protocol","genre_scores_codex":[0.7209241,0.002935318,0.01628681,0.001224063,0.0003641699,0.171733,0.06647661,0.0001997651,0.01985613],"genre_scores_gemma":[0.4899811,0.001185423,0.01467798,0.001168123,0.0001066127,0.4663925,0.02038465,0.00008503196,0.006018615],"genre_candidate":"protocol","genre_consensus":null,"teacher_disagreement_score":0.01590412,"threshold_uncertainty_score":0.05435592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07899119427791093,"score_gpt":0.3613096772338391,"score_spread":0.2823184829559281,"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."}}