Chronic low back pain: exploring trends and potential predictors
Notice bibliographique
Résumé
Context: Hundreds of thousands of Quebec residents suffer from chronic pain, for which treatment is far from optimal. Despite low back pain being the second most common reason to visit a primary care physician, management remains challenging. Additionally, chronic low back pain (CLBP) has been found to be the most common reason for patients to be referred to tertiary pain centers. Recently, there has been an increasing acceptance that bio-psycho-social factors play a crucial role the clinical course of CLBP. Objectives: The purpose of this study was to identify subgroups of CLBP patients treated in tertiary care, as defined by their changes in pain and disability over time, and to explore possible characteristics associated with these changes. Specific objectives were: 1) to establish whether there are distinct subgroups of patients with CLBP with different characteristics associated with change in pain and disability at 6, 12, and 24 months following an initial visit in a tertiary pain clinic; and 2) to identify potential social, psychological, biological, and environmental factors that may predict their responses in pain intensity and disability in accordance with the Revised Wilson and Cleary Model for Health-Related Quality of Life. Design: Observational prospective design to follow a cohort of patients who were enrolled in the web-based Quebec Pain Registry. Setting: The Quebec Pain Registry, a research database comprised of close to 5000 chronic pain patients. Eligible participants included all patients who 1) have been diagnosed with lumber without radicular pain, LBP (diagnostic code 3.1), lumbar & radicular pain, LRP (diagnostic code 3.2), or diffuse lumbar pain, DLP (diagnostic code 3.4), 2) who provided written consent for their data to be used for research purposes, and 3) have completed their initial visit to the pain clinic by May 31, 2011. Intervention: The data required for this project had previously been collected and entered in the Quebec Pain Registry. Basic descriptive results were produced using SAS® software 9.2. This analysis described the characteristics of the 917 patients included in the study at baseline. Additional data were explored to examine patterns of changes over two years for certain characteristics. A generalized estimating equations model (GEE) was used to analyze data at 6, 12, and 24 months after the initial visit. Results: 299 (32.6%) patients were diagnosed LBP, 522 (56.9%) with LRP, and 96 (10.4%) with DLP. In general, all patients were relatively comparable in terms of their characteristics with the exception of DLP, where proportions were noticeably different. Patients diagnosed with DLP had a higher pain duration median (6.0 years) and the most frequently current employment status was permanent disability (both in regards to proportions). The most common ethnicity was Caucasian among all diagnoses. Income was similarly distributed among all groups and secondary school was the highest level of education completed for all. The top three medical conditions reported other than CLBP were rheumatoid arthritis/osteoarthritis, hypertension, and depressive disorders. DLP patients reported "accident at work" as the most common circumstance surrounding their onset of pain. DLP also had noticeably different mean scores for average pain, worst pain, depression, catastrophizing, disability, mental and physical summary scores on the health-related quality of life questionnaire at baseline, 6, 12, and 24 months (in regards to proportions). Patients with higher worst pain scores, longer pain duration, and lower physical summary scores at the initial visit were significantly less likely to show improvements in pain intensity and disability at six and 12 months. Conclusions: Although modifying the analysis prohibited conclusions for a two-year follow to be made, characteristics, such as worst pain, pain duration, and lower physical summary scores at both six and 12 months were discovered.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».