Preoperative opioid use is associated with worse patient outcomes after Total joint arthroplasty: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: A significant number of patients use opioids prior to total joint arthroplasty (TJA) in North America and there is growing concern that preoperative opioid use negatively impacts postoperative patient outcomes after surgery. This systematic review and meta-analysis evaluated the current evidence investigating the influence of preoperative opioid use on postoperative patient-reported outcomes (PRO) after total joint arthroplasty. METHODS: A systematic search was performed using Ovid, Embase, Cochrane Library, Scopus, Web of Science Core Collection, CINAHL on February 15th, 2018. Studies reporting baseline and postoperative PRO among those prescribed preoperative opioids and those who were not prior to total knee and hip arthroplasty were included. Standardized mean differences (SMD) in absolute difference and relative change in PRO measures between the two groups was calculated using random effect models. RESULTS: Six studies were included (n = 7356 patients); overall 24% of patients were prescribed preoperative opioids. Patients with preoperative opioid use had worse absolute postoperative PRO scores when compared to those with no preoperative opioid use (standardized mean difference (SMD) -0.53, 95% Confidence interval (CI) -0.75, - 0.32, p < 0.0001). When relative change in PRO score was analyzed, as measured by difference between postoperative and preoperative PRO scores, there was no group differences (SMD -0.26, 95% CI -0.56, 0.05, p = 0.10). CONCLUSION: Patients prescribed preoperative opioids may attain worse overall pain and function benefits after TJA when compared to opioid-naïve patients, but do still benefit from undergoing TJA. These results suggest preoperative opioid users should be judiciously counselled regarding potential postoperative pain and function improvements after TJA.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.031 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".