{"id":"W4414077135","doi":"10.1093/braincomms/fcag146","title":"Convergent structural brain alterations in chronic pain: A multi-metric individual participant data meta-analysis","year":2025,"lang":"en","type":"article","venue":"Brain Communications","topic":"Musculoskeletal pain and rehabilitation","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Neurological Disorders and Stroke; National Institute on Drug Abuse; Natural Sciences and Engineering Research Council of Canada; Northwestern University","keywords":"Chronic pain; Large sample; Brain morphometry; Neuroimaging; Entorhinal cortex; Brain anatomy; Chronic disease; Brain size","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":[0.003371853,0.0001979793,0.0007412491,0.001100783,0.0002915557,0.00006175494,0.001128631,0.000110277,0.0004023859],"category_scores_gemma":[0.004888606,0.0001631761,0.0005224695,0.003613503,0.0002614727,0.0001800483,0.0007082781,0.0003967057,0.00001909653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001396455,"about_ca_system_score_gemma":0.0004664829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006746402,"about_ca_topic_score_gemma":0.008515717,"domain_scores_codex":[0.9964648,0.001692614,0.000822366,0.0004482543,0.0002774559,0.0002945309],"domain_scores_gemma":[0.9909694,0.003995549,0.0001519028,0.004615989,0.0001464943,0.0001206738],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"meta_analysis","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001949627,0.005687444,0.07219982,0.002597154,0.6500627,0.0000150883,0.01718246,0.01488893,0.009786529,0.0824036,0.1039277,0.04105365],"study_design_scores_gemma":[0.001986649,0.000145976,0.3907756,0.00004994497,0.08371012,0.000001268633,0.001029895,0.5041664,0.00002326267,0.0004631964,0.01732187,0.0003258216],"study_design_candidate":"meta_analysis","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.07518842,0.08337732,0.0564208,0.7728044,0.0003505521,0.007728111,0.001376423,0.0004885645,0.002265388],"genre_scores_gemma":[0.9788525,0.00008365561,0.01165225,0.004185242,0.00002202012,0.0003281408,0.003365115,0.00001345715,0.001497625],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9036641,"threshold_uncertainty_score":0.6654127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2515839297548555,"score_gpt":0.437480182874501,"score_spread":0.1858962531196455,"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."}}