{"id":"W4362684385","doi":"10.2196/45849","title":"Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach","year":2023,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Mental Health via Writing","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute for Health Research Applied Research Collaboration South London; Medical Research Council; Department of Health and Social Care; National Institute for Health and Care Research; King's College London; UK Research and Innovation; King's College Hospital NHS Foundation Trust","keywords":"Mental health; Annotation; Electronic health record; Association (psychology); Health care; Medicine; Psychology; Natural language processing; Psychiatry; Artificial intelligence; Computer science; Political science; Psychotherapist","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":[0.004654876,0.00135648,0.001097143,0.008092559,0.003124317,0.003150626,0.002840067,0.002640678,0.01374521],"category_scores_gemma":[0.02201188,0.001119229,0.001169848,0.006211021,0.002113053,0.003707351,0.00420905,0.003535698,0.007296403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003073119,"about_ca_system_score_gemma":0.006855624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01823978,"about_ca_topic_score_gemma":0.03132097,"domain_scores_codex":[0.9948425,0.001711731,0.0009210115,0.001442976,0.0008622119,0.0002196131],"domain_scores_gemma":[0.9645054,0.02468213,0.001875896,0.001957778,0.006415493,0.0005633471],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001408947,0.001601606,0.01638598,0.02163343,0.0003132786,0.01633716,0.03215754,0.01326501,0.1081002,0.01487751,0.3128493,0.4610701],"study_design_scores_gemma":[0.0006642384,0.0006313081,0.06392217,0.003529064,0.0005609259,0.008300224,0.02448641,0.09030047,0.0506175,0.01074233,0.7456132,0.0006321599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.2915516,0.004155505,0.2144279,0.008004416,0.00211961,0.01318914,0.4081834,0.01795047,0.04041796],"genre_scores_gemma":[0.1401201,0.00104423,0.4423537,0.0009871711,0.0003040315,0.008213059,0.398093,0.001195002,0.007689761],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01823978,"threshold_uncertainty_score":0.04598236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1034086382351419,"score_gpt":0.4924259535444163,"score_spread":0.3890173153092744,"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."}}