{"id":"W2904206377","doi":"10.2196/11734","title":"RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices","year":2018,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":254,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"European Commission; University College London; King's College London; National Institute for Health and Care Research; Medical Research Council; European Federation of Pharmaceutical Industries and Associations; Maudsley Charity; South London and Maudsley NHS Foundation Trust","keywords":"mHealth; Scalability; Computer science; Software deployment; Wearable computer; Data collection; Mobile device; Wearable technology; Modular design; Embedded system; World Wide Web; Database; Health care; Operating system","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.002115276,0.0003499574,0.0006956015,0.0002575524,0.001902408,0.0003341017,0.0004495327,0.0001704711,0.00004486207],"category_scores_gemma":[0.00004411611,0.0003510176,0.000037576,0.000395072,0.0002726339,0.0006723494,0.0005960254,0.0003301256,0.000009251454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002850608,"about_ca_system_score_gemma":0.0005837363,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008386103,"about_ca_topic_score_gemma":0.001049963,"domain_scores_codex":[0.996165,0.0002095977,0.001029068,0.001149562,0.0002061014,0.00124068],"domain_scores_gemma":[0.996977,0.0003429596,0.0007182504,0.000757267,0.0001026752,0.001101882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002269167,0.001682137,0.07620055,0.0129954,0.0002433321,0.00001126521,0.01319231,0.000007406949,0.00002565112,0.002351631,0.02702233,0.8639988],"study_design_scores_gemma":[0.01739375,0.03961643,0.06604089,0.00548103,0.0004154013,0.00114781,0.03233151,0.007342229,0.0002108338,0.001932652,0.8255898,0.002497623],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9757553,0.01610667,0.0003589594,0.00057679,0.0009586523,0.005072793,0.0006935603,0.0001123674,0.0003649461],"genre_scores_gemma":[0.9875072,0.0006128015,0.008081465,0.0008202624,0.0006479963,0.0007598141,0.000151823,0.00009385741,0.001324797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8615012,"threshold_uncertainty_score":0.9998942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2033862886764795,"score_gpt":0.5021420687067047,"score_spread":0.2987557800302252,"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."}}