{"id":"W4280500719","doi":"10.1016/j.isci.2022.104390","title":"A graph-embedded topic model enables characterization of diverse pain phenotypes among UK biobank individuals","year":2022,"lang":"en","type":"article","venue":"iScience","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Medical Research Council; McGill University","keywords":"Biobank; Autoencoder; Phenotype; Chronic pain; Graph; Computer science; Medicine; Data science; Data mining; Machine learning; Artificial intelligence; Bioinformatics; Theoretical computer science; Artificial neural network; Psychiatry; Gene; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0004079562,0.00006730526,0.00008574302,0.00004992485,0.0001489299,0.00001095555,0.0002830595,0.00004731204,0.00003667728],"category_scores_gemma":[0.0001230661,0.00005976663,0.00003859646,0.0001888889,0.0002758411,0.000003733094,0.000242168,0.00005127982,5.003614e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005973385,"about_ca_system_score_gemma":0.00005298561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001901307,"about_ca_topic_score_gemma":0.000005291674,"domain_scores_codex":[0.9992403,0.00007688563,0.0001215708,0.0002199345,0.0001886922,0.0001526236],"domain_scores_gemma":[0.9996706,0.00001157028,0.00008509258,0.0001699979,0.00002596941,0.0000368112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001277654,0.00006916117,0.02142129,0.00001166556,0.00001376488,0.00000160872,0.0008535448,0.0008337873,0.9460062,0.0001687208,0.0008527696,0.02975472],"study_design_scores_gemma":[0.001839472,0.002384334,0.2349568,0.00007104022,0.00008009028,0.00001586871,0.005072166,0.04550444,0.6809071,0.003886756,0.02398321,0.001298739],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9860958,0.0001825409,0.01323933,0.00009631011,0.00009582528,0.00006623069,0.00005990129,0.0000113573,0.0001526778],"genre_scores_gemma":[0.9979768,0.00002406722,0.001273801,0.0002118767,0.00002399112,0.00001671154,0.0000672548,0.000003286387,0.0004021426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2650991,"threshold_uncertainty_score":0.2437212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01710560067355207,"score_gpt":0.2458877478001447,"score_spread":0.2287821471265926,"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."}}