Impact of Perioperative Epidural Placement on Postdischarge Opioid Use in Patients Undergoing Abdominal Surgery
Bibliographic record
Abstract
BACKGROUND: Opioids play a crucial role in providing analgesia throughout the perioperative period; however, patients may become persistent users of these medications months after surgery. Epidurals have been posited to prevent the development of persistent pain, but there are little data on the effect of epidurals on persistent opioid use. METHODS: This study was conducted using a claims database of a large, nationwide commercial health insurer. Opioid-naive patients who underwent open abdominal surgery from January 2004 to December 2013 were included in the study. Propensity scores for epidural placement were calculated accounting for demographic characteristics, resource utilization, and comorbid conditions (including medical, psychiatric, and pain conditions). Time-to-event analysis was used with the primary outcome defined as 30 days without filling an opioid prescription after discharge. In addition, total morphine equivalents dispensed within 90 days of discharge were also calculated for each patient. RESULTS: A total of 6,432 patients were included in the final propensity score-matched cohort. The Cox proportional hazards ratio was 0.96 (95% CI, 0.91 to 1.01; P = 0.0910) for the relation between epidural placement and time till a 30-day gap without filling an opioid prescription. There was no difference in the total morphine equivalents dispensed within 90 days of discharge between the groups (P = 0.7670). CONCLUSIONS: Epidural placement was not protective against persistent opioid use in a large cohort of opioid-naive patients undergoing abdominal surgery. This finding does not detract from the other potential benefits of epidural placement. More research is needed to understand the mechanism of persistent opioid use after surgery and its prevention.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".