Opioid Use After Discharge in Postoperative Patients
Bibliographic record
Abstract
BACKGROUND: Over the past 2 decades, there has been an increase in opioid use and subsequently, opioid deaths. The amount of opioid prescribed to surgical patients has also increased. The aim of this systematic review was to determine postdischarge opioid consumption in surgical patients compared with the amount of opioid prescribed. Secondary outcomes included adequacy of pain control and disposal methods for unused opioids. OBJECTIVE: The objective of this study is to characterize postdischarge opioid consumption and prescription patterns in surgical patients. METHODS: A systematic search in MEDLINE and EMBASE identified 11 patient survey studies reporting on postdischarge opioid use in 3525 surgical patients. RESULTS: The studies reported on a variety of surgical operations, including abdominal surgery, orthopedic procedures, tooth extraction, and dermatologic procedures. The majority of patients consumed 15 pills or less postdischarge. The proportion of used opioids ranged from 5.6% to 59.1%, with an outlier of 90.1% in pediatric spinal fusion patients. Measured pain scores of those taking opioids ranged between 2 and 5 out of 10 and the majority of patients were satisfied with their pain control. Seventy percent of patients kept the excess opioids. Where planned disposal methods were reported, between 4% and 59% of patients planned proper disposal. CONCLUSION: This study suggests that surgical patients are using substantially less opioid than prescribed. There is a lack of awareness regarding proper disposal of leftover medication, leaving excess opioid that may be used inappropriately by the patient or others. Education for providers and clinical practice guidelines that provide guidance on prescription of outpatient of opioids are required.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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; 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".