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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| 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".