Bibliographic record
Abstract
BACKGROUND: This study evaluated the risk of long-term analgesic use after low-risk surgery in older adults not previously prescribed analgesics. METHODS: We conducted a retrospective cohort study using linked, population-based administrative data in Ontario, Canada, from April 1, 1997, through December 31, 2008. We identified Ontario residents 66 years and older who were dispensed an opioid within 7 days of a short-stay surgery (cataract surgery, laparoscopic cholecystectomy, transurethral resection of the prostate, or varicose vein stripping) and assessed the risk of long-term opioid use, defined as a prescription for an opioid within 60 days of the 1-year anniversary of the surgery. In a secondary analysis, we examined the risk of long-term use of nonsteroidal anti-inflammatory drugs (NSAIDs). We used multivariate logistic regression to examine the association between postsurgical use of analgesics and long-term use. RESULTS: Among 391,139 opioid-naive patients undergoing short-stay surgery, opioids were newly prescribed to 27,636 patients (7.1%) within 7 days of being discharged from the hospital, and opioids were prescribed to 30,145 patients (7.7%) at 1 year from surgery. An increase in the use of oxycodone was found during this time (from 5.4% within 7 days to 15.9% at 1 year). In our primary analysis, patients receiving an opioid prescription within 7 days of surgery were 44% more likely to become long-term opioid users within 1 year compared with those who received no such prescription (adjusted odds ratio, 1.44; 95% CI, 1.39-1.50). In a secondary analysis, among 383,780 NSAID-naive patients undergoing short-stay surgery, NSAIDs were prescribed to 1169 patients (0.3%) within 7 days of discharge and to 30,080 patients (7.8%) at 1 year from surgery. Patients who began taking NSAIDs within 7 days of surgery were almost 4 times more likely to become long-term NSAID users compared with patients with no such prescription (adjusted odds ratio, 3.74; 95% CI, 3.27-4.28). CONCLUSION: Prescription of analgesics immediately after ambulatory surgery occurs frequently in older adults and is associated with long-term use.
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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 | 0.000 | 0.000 |
| 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 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".