Factors associated with early opioid dispensing compared with NSAID and muscle relaxant dispensing after a work-related low back injury
Bibliographic record
Abstract
OBJECTIVES: The objective of this historical cohort study was to determine the claimant and prescriber factors associated with receiving opioids at first postinjury dispense compared with non-steroidal anti-inflammatory drugs (NSAIDs) and skeletal muscle relaxants (SMRs) in a sample of workers' compensation claimants with low back pain (LBP) claims between 1998 and 2009 in British Columbia, Canada. METHODS: Administrative workers' compensation, prescription and healthcare data were linked. The association between claimant factors (sociodemographics, occupation, diagnosis, comorbidities, pre-injury prescriptions and healthcare) and prescriber factors (sex, birth year, specialty) with drug class(es) at first dispense (opioids vs NSAIDs/SMRs) was examined with multilevel multinomial logistic regression. RESULTS: Increasing days supplied with opioids in the previous year was associated with increased odds of receiving opioids only (1-14 days OR 1.62, 95% CI 1.51 to 1.75; ≥15 days OR 5.12, 95% CI 4.65 to 5.64) and opioids with NSAIDs/SMRs (1-14 days OR 1.49, 95% CI 1.39 to 1.60; ≥15 days OR 2.82, 95% CI 2.56 to 3.12). Other significant claimant factors included: pre-injury dispenses for NSAIDs, SMRs, antidepressants, anticonvulsants and sedative-hypnotics/anxiolytics; International Statistical Classification of Diseases and Related Health Problems, 9th Revision diagnosis; various pre-existing comorbidities; prior physician visits and hospitalisations; and year of injury, age, sex, health authority and occupation. Prescribers accounted for 25%-36% of the variability in the drug class(es) received, but prescriber sex, specialty and birth year did not explain observed between-prescriber variation. CONCLUSIONS: During this period in the opioid crisis, early postinjury dispensing was multifactorial, with several claimant factors associated with receiving opioids at first prescription. Prescriber variation in drug class choice appears particularly important, but was not explained by basic prescriber characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".