{"id":"W3166331525","doi":"10.14283/jpad.2021.30","title":"A UK-Wide Study Employing Natural Language Processing to Determine What Matters to People about Brain Health to Improve Drug Development: The Electronic Person-Specific Outcome Measure (ePSOM) Programme","year":2021,"lang":"en","type":"article","venue":"The Journal of Prevention of Alzheimer s Disease","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Population and Public Health","funders":"","keywords":"Measure (data warehouse); Drug; Natural (archaeology); Drug development; Outcome (game theory); Psychology; Medicine; Computer science; Psychiatry; Economics; History; Data mining; Microeconomics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003007738,0.0002261506,0.0004441187,0.0002383294,0.0002709821,0.0001901537,0.0003284964,0.00001800041,0.000123881],"category_scores_gemma":[0.0002723034,0.0001402981,0.0002311405,0.0006244484,0.00003032274,0.0002738085,0.0001519289,0.0003987328,0.00001853841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000188168,"about_ca_system_score_gemma":0.001166382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001907207,"about_ca_topic_score_gemma":0.0001969298,"domain_scores_codex":[0.9965895,0.0005788835,0.000752652,0.000264682,0.001188435,0.0006258684],"domain_scores_gemma":[0.9979159,0.0001150904,0.0003626618,0.0003215195,0.0006400107,0.0006448198],"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":[0.003896304,0.002575498,0.08338325,0.0003844801,0.001806012,0.0001795697,0.07747628,0.00001966807,0.00864151,0.000004161618,0.002937501,0.8186958],"study_design_scores_gemma":[0.00362646,0.001929173,0.9038398,0.001836228,0.001157816,0.00008852944,0.08082922,0.00002530785,0.005183456,0.0000170025,0.001164049,0.0003029566],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9277059,0.0194245,0.0009791795,0.04936728,0.0001405822,0.002357966,0.00000318927,0.00001477486,0.000006659032],"genre_scores_gemma":[0.9928627,0.00004203132,0.0004473692,0.00587307,0.0001093791,0.00008314545,0.000007097875,0.00003447353,0.0005407233],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8204566,"threshold_uncertainty_score":0.5721189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03831597131853096,"score_gpt":0.3542057859354552,"score_spread":0.3158898146169242,"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."}}