Age and postoperative opioid prescriptions: a population‐based cohort study of opioid‐naïve adults
Bibliographic record
Abstract
PURPOSE: Opioids are commonly prescribed for acute pain after surgery. However, it is unclear whether these prescriptions are usually modified to account for patient age and, in particular, opioid-related risks among older adults. We therefore sought to describe postoperative opioid prescriptions filled by opioid-naïve adults undergoing four common surgical procedures. METHODS: This retrospective cohort study used individually linked surgery and prescription opioid dispensing data from Ontario, Canada to create a population-based sample of 135 659 opioid-naïve adults who underwent one of four surgical procedures (laparoscopic cholecystectomy, laparoscopic appendectomy, knee meniscectomy, or breast excision) between 2013 and 2017. Patient age, in years, was categorized as 18 to 64, 65 to 69, 70 to 74, and 75 and over. Postoperative opioid prescriptions were identified as those filled on or within 6 days of surgical discharge date. For those who filled a prescription, we assessed the total morphine milligram equivalent (MME) dose, types of opioids, and any subsequent opioid prescriptions filled within 30 days of surgical discharge date. Results were presented stratified by surgical procedure. RESULTS: For three of the four surgical procedures we assessed, the proportion of patients who filled a postoperative opioid prescription decreased with age (P < 0.001 for trend), and there was a small shift in the type of opioid (more codeine or tramadol and less oxycodone; P < 0.001 for trend). However, the total MME dose of the initial prescription(s) filled showed minimal age-related trends. CONCLUSIONS: The proportion of opioid-naïve patients filling postoperative opioid prescriptions decreases with age. However, postoperative opioid prescription dosage is not typically different in older adults.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".