Opioid prescribing practices prior to elective foot and ankle surgery: a population-based evaluation using health administrative data from a tertiary hospital in Canada
Bibliographic record
Abstract
BACKGROUND: Complex elective foot and ankle surgery is known to be painful so most patients are prescribed opioids at the time of surgery; however, the number of patients prescribed opioids while waiting for surgery in Canada is unknown. Our primary objective was to describe the pre and postoperative prescribing practices for patients in Alberta, Canada undergoing complex elective foot and ankle surgery. Secondarily, we evaluated postoperative opioid usage and hospital outcomes. METHODS: In this population-based retrospective analysis, we identified all adult patients who underwent unilateral elective orthopedic foot and ankle surgery at a single tertiary hospital between May 1, 2015 and May 31, 2017. Patient and surgical data were extracted from a retrospective chart review and merged with prospectively collected, individual level drug dispensing administrative data to analyze opioid dispensing patterns, including dose, duration, and prescriber for six months before and after foot and ankle surgery. RESULTS: Of the 100 patients, 45 had at least one opioid prescription dispensed within six months before surgery, and of these, 19 were long-term opioid users (> 90 days of continuous use). Most opioid users obtained opioid prescriptions from family physicians both before (78%) and after (65%) surgery. No preoperative non-users transitioned to long-term opioid use postoperatively, but 68.4% of the preoperative long-term opioid users remained long-term opioid users postoperatively. During the index hospitalization, preoperative long-term opioid users consumed higher doses of opioids (99.7 ± 120.5 mg/day) compared to opioid naive patients (28.5 ± 36.1 mg/day) (p < 0.001). Long-term opioid users stayed one day longer in hospital than opioid-naive patients (3.9 ± 2.8 days vs 2.7 ± 1.1 days; p = 0.01). CONCLUSIONS: A significant number of patients were dispensed opioids before and after foot and ankle surgery with the majority of prescriptions coming from primary care practitioners. Patients who were prescribed long-term opioids preoperatively were more likely to continue to use opioids at follow-up and required larger in-hospital opioid dosages and stayed longer in hospital. Further research and education for both patients and providers are needed to reduce the community-based prescribing of opioid medication pre-operatively and provide alternative pain management strategies prior to surgery to improve postoperative outcomes and reduce long-term postoperative opioid use.
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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) | 0.000 | 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".