The impact of pre‐operative depression on pain outcomes after major surgery: a systematic review and meta‐analysis
Bibliographic record
Abstract
Summary Symptoms of depression are common among patients before surgery. Depression may be associated with worse postoperative pain and other pain‐related outcomes. This review aimed to characterise the impact of pre‐operative depression on postoperative pain outcomes. We conducted a systematic review of observational studies that reported an association between pre‐operative depression and pain outcomes after major surgery. Multilevel random effects meta‐analyses were conducted to pool standardised mean differences and 95%CI for postoperative pain scores in patients with depression compared with those without depression, at different time intervals. A meta‐analysis was performed for studies reporting change in pain scores from the pre‐operative period to any time‐point after surgery. Sixty studies (n = 501,962) were included in the overall review, of which 18 were eligible for meta‐analysis. Pre‐operative depression was associated with greater pain scores at < 72 h (standardised mean difference 0.97 (95%CI 0.37–1.56), p = 0.009, I 2 = 41%; moderate certainty) and > 6 months (standardised mean difference 0.45 (95%CI 0.23–0.68), p < 0.001, I 2 = 78%; low certainty) after surgery, but not at 3–6 months after surgery (standardised mean difference 0.54 (95%CI ‐0.06–1.15), p = 0.07, I 2 = 83%; very low certainty). The change in pain scores from pre‐operative baseline to 1–2 years after surgery was similar between patients with and without pre‐operative depression (standardised mean difference 0.13 (95%CI ‐0.06–0.32), p = 0.15, I 2 = 54%; very low certainty). Overall, pre‐existing depression before surgery was associated with worse pain severity postoperatively. Our findings highlight the importance of incorporating psychological care into current postoperative pain management approaches in patients with depression.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".