Prevalence of Early Opioid Prescribing for Non-Specific Low Back Pain and \nDisability Duration: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".