Prescription Dispensing Patterns Before and After a Workers’ Compensation Claim
Bibliographic record
Abstract
OBJECTIVE: Compare prescription dispensing before and after a work-related low back injury. METHODS: Descriptive analyses were used to describe opioid, nonsteroidal anti-inflammatory drug (NSAID), and skeletal muscle relaxant (SMR) dispensing 1 year pre- and post-injury among 97,124 workers in British Columbia with new workers' compensation low back claims from 1998 to 2009. RESULTS: Before injury, 19.7%, 21.2%, and 6.3% were dispensed opioids, NSAIDs, and SMRs, respectively, increasing to 39.0%, 50.2%, and 28.4% after. Median time to first post-injury prescription was less than a week. Dispensing was stable pre-injury, followed by a sharp increase within 8 weeks post-injury. Dispensing dropped thereafter, but remained elevated nearly a year post-injury, an increase attributable to less than 2% of claimants. CONCLUSION: These drug classes are commonly dispensed, particularly shortly after injury and dispensing is of short duration for most, though a small subgroup receives prolonged courses.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".