Age and postoperative opioid use in women undergoing pelvic organ prolapse surgery
Bibliographic record
Abstract
INTRODUCTION: Our objective was to explore the relation between patient age and postoperative opioid use up to 24 hours following pelvic organ prolapse (POP) surgery. MATERIAL AND METHODS: We conducted a prospective cohort study following 335 women ranging in age from 26 to 82 years who underwent surgery for multi-compartment POP at a tertiary center in Alberta, Canada. Patient characteristics were measured using baseline questionnaires. Perioperative data were collected from medical chart review during and up to 24 hours following surgery. We used logistic regression to analyze the odds of being opioid-free and linear regression to analyze mean differences in opioid dose, measured as total morphine equivalent daily dose, exploring for a potential non-linear effect of age. Adjusted models controlled for preoperative pain, surgical characteristics and patient health factors. RESULTS: Overall, age was positively associated with greater odds of being opioid-free in the first 24 hours after surgery (adjusted odds ratio per increasing year of age = 1.07, 95% confidence interval [CI] 1.04-1.09, n = 332 women). Among opioid users, age was inversely associated with total opioid dose (adjusted mean difference per increasing year of age = 0.71 mg morphine equivalent daily dose, 95% CI -0.99 to -0.44, n = 204 women). There was no evidence of a non-linear relation between age and postoperative opioid use or dose. CONCLUSIONS: In the context of POP surgery, we found that younger women were more likely to use opioids after surgery and to use a higher dose in the first 24 hours when compared with older women. These findings support physicians to consider age when counseling POP patients regarding pain management after surgery, and to direct resources aimed at opioid-free pain control towards younger patients.
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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 | 0.000 | 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".