{"id":"W7084038063","doi":"10.1371/journal.pone.0265970\">10.1371/journal.pone.0265970</a","title":"Derivation of clinical prediction rules for identifying patients with non-acute low back pain who respond best to a lumbar stabilization exercise program at post-treatment and six-month follow-up","year":2022,"lang":"en","type":"article","venue":"","topic":"Ginger and Zingiberaceae research","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke; Hôpital Charles-Le Moyne; McGill University; Statistics Canada; Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal; Centre for Interdisciplinary Research in Rehabilitation; Institut de recherche Robert-Sauvé en santé et en sécurité du travail","funders":"Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail","keywords":"Low back pain; Clinical prediction rule; Logistic regression; Lumbar; Trunk; Population; Back pain; Lumbar spine; Manual therapy","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":[0.001699797,0.0001927728,0.0003571269,0.0001783468,0.0005181537,0.00003472431,0.0001213054,0.0001331791,0.0004455196],"category_scores_gemma":[0.0001393039,0.0001674434,0.00009628738,0.0002710356,0.00007779448,0.0001727628,0.0001713755,0.0002095895,0.000009362382],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001946334,"about_ca_system_score_gemma":0.0001137793,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004476035,"about_ca_topic_score_gemma":0.0001713574,"domain_scores_codex":[0.9977364,0.0005948012,0.0005637753,0.0004532336,0.0002856747,0.0003661277],"domain_scores_gemma":[0.998623,0.0004162228,0.0001790873,0.0001852265,0.000367419,0.0002290477],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.01529502,0.003265981,0.8152892,0.0003783213,0.0004700071,0.000004915743,0.003217633,0.0005660204,0.006534296,0.00002629622,0.002080446,0.1528718],"study_design_scores_gemma":[0.04636235,0.05037021,0.7527394,0.0003174366,0.001474274,0.000009369931,0.002842331,0.07430095,0.02221767,0.00009474443,0.0481762,0.001095044],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933248,0.00007372897,0.0008943696,0.0002042898,0.0003466574,0.004499656,0.0005050509,0.00004576679,0.0001057469],"genre_scores_gemma":[0.9923799,0.0001677512,0.002252725,0.0001812329,0.00005558529,0.001494474,0.001003243,0.00003948799,0.002425581],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1517768,"threshold_uncertainty_score":0.6828144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1252299190509076,"score_gpt":0.4701427092235744,"score_spread":0.3449127901726668,"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."}}