Prevalence of Early Opioid Prescribing for Non-Specific Low Back Pain and \nDisability Duration: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
Background: Low back pain (LBP) is a major public health issue, which affects most \npeople at some point in their lives. LBP poses huge burden on the society in terms of \neconomic burden because of workdays lost due to disabilities, loss of productivity, \npermanents disability, and increased risk of mental health conditions. Length of \ndisability (LOD) due to occupational LBP or non-specific LBP (NSLBP) is related to \nseveral factors including individual factors, work related factors and healthcare \nrelated factors that are not abided by the clinical guidelines such as early magnetic \nresonance imaging (eMRI) scanning and early prescription of opioid (within first 15 \ndays of seeking medical care), which were found to be significant predictors of \nincreased LOD. \nAim: The aim of this thesis was to systematically review and summarize the findings \nof epidemiologic studies assessing the prevalence of early opioid prescribing for LBP \nand the relationship between early opioid prescribing for LBP and LOD. \nMethods: Electronic bibliographic databases were searched from inception to June \n2020 (Medline, EMBASE, Psych INFO, and CINAHL). These databases were \nsearched using Medical Subject Headings (MeSH) or Emtree terms and free-text \nterms. The Web of Science citation index, Google scholar and ResearchGate were \nalso searched using relevant key terms to identify any additional eligible studies for inclusion in the review. Two reviewers independently selected eligible studies, \nextracted data, and assessed the methodological quality of included studies using the \nNewcastle-Ottawa Scale (NOS). Due to high degree of heterogeneity between studies, \nrandom effects model (REM) was used to pool the results. Sensitivity analysis was \nalso performed for assessing the causes of heterogeneity. \nResults: A total of seven cohort studies were included in this meta-analysis. The \noverall methodological quality of included studies was found to be good. The pooled \nprevalence of early opioid prescribing for acute LBP was 20% (95% CI: 10.8-32.1%), \nQ=12071.2, p-value <0.001, and Higgin's I2=100%. Only three studies examined the \nrelationship between early opioid prescribing for LBP and LOD. The three study \nreported an association between early opioid prescribing for acute LBP and LOD, \nwith an evidence of a dose-response relationship. \nConclusion: The findings of this systematic review show that one in five patients with \nacute LBP are prescribed opioid early in the medical care. These findings suggest that \nincompliance with clinical guidelines recommendations, which discourage early \nopioid prescribing for acute LBP early in the care, is common and is associated with \nincreased work disability duration. Future research on early opioid prescription for \nLBP and the relationship with prolonging disability should account for all-important \nfactors associated with LOD in this population to better estimate the effect of early \nopioid prescription on length of disability. Further research aiming at uncovering the \nreasons for incompliance with current guidelines is needed. In addition, Developing \nand testing healthcare quality improvement interventions to enhance compliance with \nv \nclinical guidelines about early opioid prescribing for LBP may help in preventing \nprolonged 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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».