Prevalence of Early Opioid Prescribing for Non-Specific Low Back Pain andDisability Duration: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
Background: Low back pain (LBP) is a major public health issue, which affects most people at some point in their lives. LBP poses huge burden on the society in terms of economic burden because of workdays lost due to disabilities, loss of productivity, permanents disability, and increased risk of mental health conditions. Length of disability (LOD) due to occupational LBP or non-specific LBP (NSLBP) is related to several factors including individual factors, work related factors and healthcare related factors that are not abided by the clinical guidelines such as early magnetic resonance imaging (eMRI) scanning and early prescription of opioid (within first 15 days of seeking medical care), which were found to be significant predictors of increased LOD. Aim: The aim of this thesis was to systematically review and summarize the findings of epidemiologic studies assessing the prevalence of early opioid prescribing for LBP and the relationship between early opioid prescribing for LBP and LOD. Methods: Electronic bibliographic databases were searched from inception to June 2020 (Medline, EMBASE, Psych INFO, and CINAHL). These databases were searched using Medical Subject Headings (MeSH) or Emtree terms and free-text terms. The Web of Science citation index, Google scholar and ResearchGate were also searched using relevant key terms to identify any additional eligible studies for inclusion in the review. Two reviewers independently selected eligible studies, extracted data, and assessed the methodological quality of included studies using the Newcastle-Ottawa Scale (NOS). Due to high degree of heterogeneity between studies, random effects model (REM) was used to pool the results. Sensitivity analysis was also performed for assessing the causes of heterogeneity. Results: A total of seven cohort studies were included in this meta-analysis. The overall methodological quality of included studies was found to be good. The pooled prevalence of early opioid prescribing for acute LBP was 20% (95% CI: 10.8-32.1%), Q=12071.2, p-value <0.001, and Higgin's I2=100%. Only three studies examined the relationship between early opioid prescribing for LBP and LOD. The three study reported an association between early opioid prescribing for acute LBP and LOD, with an evidence of a dose-response relationship. Conclusion: The findings of this systematic review show that one in five patients with acute LBP are prescribed opioid early in the medical care. These findings suggest that incompliance with clinical guidelines recommendations, which discourage early opioid prescribing for acute LBP early in the care, is common and is associated with increased work disability duration. Future research on early opioid prescription for LBP and the relationship with prolonging disability should account for all-important factors associated with LOD in this population to better estimate the effect of early opioid prescription on length of disability. Further research aiming at uncovering the reasons for incompliance with current guidelines is needed. In addition, Developing and testing healthcare quality improvement interventions to enhance compliance with v clinical guidelines about early opioid prescribing for LBP may help in preventing prolonged disability and its associated negative impacts in patients with acute LBP.
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 | 0,013 | 0,039 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,018 | 0,040 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».