The Impact of Preoperative Patient Education on Postoperative Pain, Opioid Use, and Psychological Outcomes: A Narrative Review
Bibliographic record
Abstract
Background: Recent studies have shown that preoperative education can positively impact postoperative recovery, improving postoperative pain management and patient satisfaction. Gaps in preoperative education regarding postoperative pain and opioid use may lead to increased patient anxiety and persistent postoperative opioid use. Objectives: The objective of this narrative review was to identify, examine, and summarize the available evidence on the use and effectiveness of preoperative educational interventions with respect to postoperative outcomes. Method: The current narrative review focused on studies that assessed the impact of preoperative educational interventions on postoperative pain, opioid use, and psychological outcomes. The search strategy used concept blocks including "preoperative" AND "patient education" AND "elective surgery," limited to the English language, humans, and adults, using the MEDLINE ALL database. Studies reporting on preoperative educational interventions that included postoperative outcomes were included. Studies reporting on enhanced recovery after surgery protocols were excluded. Results: From a total of 761 retrieved articles, 721 were screened in full and 34 met criteria for inclusion. Of 12 studies that assessed the impact of preoperative educational interventions on postoperative pain, 5 reported a benefit for pain reduction. Eight studies examined postoperative opioid use, and all found a significant reduction in opioid consumption after preoperative education. Twenty-four studies reported on postoperative psychological outcomes, and 20 of these showed benefits of preoperative education, especially on postoperative anxiety. Conclusion: Preoperative patient education interventions demonstrate promise for improving postoperative outcomes. Preoperative education programs should become a prerequisite and an available resource for all patients undergoing elective surgery.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".