Long-term opioid medication use before and after joint replacement surgery in New Zealand.
Bibliographic record
Abstract
AIM: To describe the use of opioid analgesics over three years before and after total joint replacement surgery in New Zealand. METHOD: We extracted information on all individuals undergoing publicly funded total hip or knee replacement surgery in New Zealand between June 2011 and December 2014, and linked data on opioid prescribing, from the Statistics New Zealand Integrated Data Infrastructure. We analysed monthly opioid use over the three years before and after surgery and the transition from pre-operative and/or immediate post-operative use to chronic post-operative use. RESULTS: The prevalence of opioid use increased from 7% three years before surgery to 22% immediately prior to surgery, was common (75%) in the month following surgery and declined rapidly to 10-12% per month over the following years. Patients dispensed opioids prior to surgery or in the post-operative recovery period were at significantly higher risk of subsequent chronic opioid use. CONCLUSION: Opioid analgesic prescribing was reduced following joint replacement surgery, although a substantial minority of patients remained long-term opioid users. Avoiding unnecessary pre-operative opioid use and limiting opioid use for post-operative pain management where appropriate could help to reduce the risk of potentially ineffective or harmful long-term opioid use in these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".