{"id":"W4309726586","doi":"10.1101/2022.11.21.22282474","title":"Longitudinally Tracking Personal Physiomes for Precision Management of Childhood Epilepsy","year":2022,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Epilepsy research and treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Wearable computer; Computer science; Wearable technology; Epilepsy; Precision medicine; Tracking (education); Cohort; Artificial intelligence; mHealth; Electroencephalography; Cluster analysis; Physical medicine and rehabilitation; Machine learning; Medicine; Psychology; Neuroscience; Psychiatry; Internal medicine; Embedded system","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":[0.0004500963,0.0004659554,0.0004246953,0.0009298686,0.0002119082,0.0005421969,0.0003846854,0.0004138431,0.0009865857],"category_scores_gemma":[0.001671177,0.0001390438,0.00026416,0.0007002218,0.0001434569,0.0003190417,0.0006002134,0.0004791152,0.0003093262],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000280258,"about_ca_system_score_gemma":0.0004663792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00362883,"about_ca_topic_score_gemma":0.010743,"domain_scores_codex":[0.9997837,0.00006179092,0.00002010972,0.00007105806,0.00003672177,0.00002656205],"domain_scores_gemma":[0.9992771,0.0001843567,0.0002188518,0.00009784586,0.0001558792,0.00006601807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008008426,0.0003878523,0.6125366,0.0004465732,0.0003409472,0.0007022762,0.0004735887,0.04201856,0.04861788,0.001531512,0.007600405,0.2845429],"study_design_scores_gemma":[0.00009319646,0.0005726889,0.5911361,0.0002036095,0.0002549971,0.001138464,0.0008577718,0.3611664,0.02875346,0.005567762,0.01015147,0.0001041427],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8405329,0.001664264,0.1456078,0.0009811657,0.0001085004,0.0001362421,0.007252731,0.0008849764,0.002831318],"genre_scores_gemma":[0.9612971,0.0005020042,0.03513481,0.0001252151,0.00006381999,0.0001039257,0.002219463,0.00003609088,0.0005176454],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00362883,"threshold_uncertainty_score":0.007215381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04553631218198152,"score_gpt":0.3440949639098486,"score_spread":0.2985586517278671,"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."}}