The effect of a quality improvement project on post-operative opioid use following outpatient spinal surgery
Bibliographic record
Abstract
Background: Prescribing opioids upon discharge after surgery is common practice; however, there are many inherent risks including dependency, diversion, and medical complications. Our prospective pre- and post-intervention study investigates the effect of a standardized analgesic prescription on the quantity of opioids prescribed and patients' level of pain and satisfaction with pain control in the early post-operative period. Methods: With the implementation of an electronic medical record, a standardized prescription was built employing multimodal analgesia and a stepwise approach to analgesics based on level of pain. Patients received an education handout pre-operatively explaining the prescription. Consecutive patients over a three-month period undergoing elective spine surgery as day or overnight stay cases who received usual care were compared to a similar cohort who received the standardized prescription and education. Patient satisfaction with post-operative pain control, post-operative pain scores, number of refills required, and opioids prescribed in oral morphine equivalents (OMEs) were compared before and after implementation of the standardized analgesic prescription. Results: Twenty-six patients received usual care (Control group) and 26 patients received the standardized prescription and education handout (Intervention group). There were significantly fewer OMEs prescribed in the Intervention group compared to the Control group. There was no difference between groups in: patient post-operative pain intensity score, post-operative satisfaction score, or number of refills required. Conclusions: This study demonstrates that a standardized prescription consisting of an appropriate amount of opioid and non-opioid analgesics is effective in reducing the OMEs prescribed post-operatively in elective spine surgery procedures, without compromising patient pain control or satisfaction or increasing the number of refills required.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".