Patient-Reported Opioid Analgesic Use After Discharge from Surgical Procedures: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: This systematic review synthesizes evidence on patient-reported outpatient opioid analgesic use after surgery. METHODS: We searched PubMed (February 2019) and Web of Science and Embase (June 2019) for U.S. studies describing patient-reported outpatient opioid analgesic use. Two reviewers extracted data on opioid analgesic use, standardized the data on use , and performed independent quality appraisals based on the Cochrane Risk of Bias Tool and an adapted Newcastle-Ottawa scale. RESULTS: Ninety-six studies met the eligibility criteria; 56 had sufficient information to standardize use in oxycodone 5-mg tablets. Patient-reported opioid analgesic use varied widely by procedure type; knee and hip arthroplasty had the highest postoperative opioid use, and use after many procedures was reported as <5 tablets. In studies that examined excess tablets, 25-98% of the total tablets prescribed were reported to be excess, with most studies reporting that 50-70% of tablets went unused. Factors commonly associated with higher opioid analgesic use included preoperative opioid analgesic use, higher inpatient opioid analgesic use, higher postoperative pain scores, and chronic medical conditions, among others. Estimates also varied across studies because of heterogeneity in study design, including length of follow-up and inclusion/exclusion criteria. CONCLUSION: Self-reported postsurgery outpatient opioid analgesic use varies widely both across procedures and within a given procedure type. Contributors to within-procedure variation included patient characteristics, prior opioid use, intraoperative and perioperative factors, and differences in the timing of opioid use data collection. We provide recommendations to help minimize variation caused by study design factors and maximize interpretability of forthcoming studies for use in clinical guidelines and decision-making.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".