{"id":"W4206048551","doi":"10.2196/30557","title":"Enabling Research and Clinical Use of Patient-Generated Health Data (the mindLAMP Platform): Digital Phenotyping Study","year":2022,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Digital Mental Health Interventions","field":"Psychology","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Wearable computer; Health care; Computer science; Digital health; Wearable technology; mHealth; Data science; Human–computer interaction; Embedded 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0129821,0.0002433389,0.0001799424,0.0009410945,0.0005046605,0.002938131,0.001048652,0.0009061827,0.004107011],"category_scores_gemma":[0.02675043,0.0001894363,0.0004555938,0.0009629347,0.001309108,0.002549297,0.004252675,0.00105358,0.0007986093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001385645,"about_ca_system_score_gemma":0.003042758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001654542,"about_ca_topic_score_gemma":0.001737815,"domain_scores_codex":[0.9957582,0.002719702,0.0001890403,0.0004348724,0.0006747593,0.0002233188],"domain_scores_gemma":[0.9778556,0.01399075,0.001445383,0.003097443,0.001960047,0.001650832],"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":[0.003429047,0.002428133,0.2934528,0.001643523,0.0003837798,0.002543006,0.01092859,0.003123242,0.008164737,0.04967573,0.04681862,0.5774087],"study_design_scores_gemma":[0.001785657,0.00557431,0.4381352,0.003533188,0.001081136,0.009307173,0.01548028,0.03129779,0.03176874,0.06802305,0.3936689,0.0003445052],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8618285,0.003873759,0.06165923,0.02334792,0.0004041524,0.001937914,0.007445441,0.001580466,0.03792267],"genre_scores_gemma":[0.9106482,0.002006201,0.07340555,0.006034235,0.0003182548,0.001580251,0.002585544,0.0002685098,0.003153241],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0129821,"threshold_uncertainty_score":0.06865674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5141325177443964,"score_gpt":0.5638354228083599,"score_spread":0.04970290506396347,"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."}}