Nonopioid Analgesic Prescriptions Filled after Surgery among Older Adults in Ontario, Canada: A Population-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: The objective was to assess changes over time in prescriptions filled for nonopioid analgesics for older postoperative patients in the immediate postdischarge period. The authors hypothesized that the number of patients who filled a nonopioid analgesic prescription increased during the study period. METHODS: The authors performed a population-based cohort study using linked health administrative data of 278,366 admissions aged 66 yr or older undergoing surgery between fiscal year 2013 and 2019 in Ontario, Canada. The primary outcome was the percentage of patients with new filled prescriptions for nonopioid analgesics within 7 days of discharge, and the secondary outcome was the analgesic class. The authors assessed whether patients filled prescriptions for a nonopioid only, an opioid only, both opioid and nonopioid prescriptions, or a combination opioid/nonopioid. RESULTS: Overall, 22% (n = 60,181) of patients filled no opioid prescription, 2% (n = 5,534) filled a nonopioid only, 21% (n = 59,608) filled an opioid only, and 55% (n = 153,043) filled some combination of opioid and nonopioid. The percentage of patients who filled a nonopioid prescription within 7 days postoperatively increased from 9% (n = 2,119) in 2013 to 28% (n = 13,090) in 2019, with the greatest increase for acetaminophen: 3% (n = 701) to 20% (n = 9,559). The percentage of patients who filled a combination analgesic prescription decreased from 53% (n = 12,939) in 2013 to 28% (n = 13,453) in 2019. However, the percentage who filled both an opioid and nonopioid prescription increased: 4% (n = 938) to 21% (n = 9,880) so that the overall percentage of patients who received both an opioid and a nonopioid remained constant over time 76% (n = 18,642) in 2013 to 75% (n = 35,391) in 2019. CONCLUSIONS: The proportion of postoperative patients who fill prescriptions for nonopioid analgesics has increased. However, rather than a move to use of nonopioids alone for analgesia, this represents a shift away from combination medications toward separate prescriptions for opioids and nonopioids.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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 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".