Time trends and patterns in opioid prescription use following orthopaedic surgery in Ontario, Canada, from 2004/2005 to 2017/2018: a population-based study
Bibliographic record
Abstract
OBJECTIVES: Increased use of opioids and their associated harms have raised concerns around prescription opioid use for pain management following surgery. We examined trends and patterns of opioid prescribing following elective orthopaedic surgery. DESIGN: Population-based study. SETTING: Ontario, Canada. PARTICIPANTS: Ontario residents aged 66+ years who had elective orthopaedic surgery from April 2004 to March 2018. PRIMARY AND SECONDARY OUTCOME MEASURES: Postoperative opioid use (short term: within 90 days of surgery, prolonged: within 180 days and chronic: within 1 year), specific opioids prescribed, average duration (days) and amount (morphine milligram equivalents) of the initial prescription by year of surgery. RESULTS: We included 464 460 elective orthopaedic surgeries in 2004/2005-2017/2018: 80% of patients used opioids within 1 year of surgery-25.1% were chronic users. There was an 8% increase in opioid use within 1 year of surgery, from 75.1% in 2004/2005 to 80.9% in 2017/2018: a 29% increase in short-term use and a decline in prolonged (9%) and chronic (22%) use. After 2014/2015, prescribed opioid amounts initially declined sharply, while the duration of the initial prescription increased substantially. Across categories of use, there was a steady decline in coprescription of benzodiazepines and opioids. CONCLUSIONS: Most patients filled opioid prescriptions after surgery, and many continued filling prescriptions after 3 months. During a period of general increase in awareness of opioid harms and dissemination of guidelines/policies aimed at opioid prescribing for chronic pain, we found changes in prescribing practices following elective orthopaedic surgery. Findings illustrate the potential impact of guidelines/policies on shaping prescription patterns in the surgical population, even in the absence of specific guidelines for surgical prescribing.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".