Meta-analysis of scar formation and depression and anxiety symptoms in patients after cardiac surgery
Bibliographic record
Abstract
Background The vertical median skin scar associated with median sternotomy, a standard approach for most cardiac surgeries, can cause psychological distress in patients, particularly depression and anxiety. The impact of scarring after cardiac surgery on depression and anxiety symptoms in patients is not well understood. Aim The purpose of this meta-analysis was to investigate the effect of scarring on depressive and anxiety symptoms in patients after cardiac surgery. Methods To investigate the relationship between scar formation and depression and anxiety symptoms in patients after cardiac surgery. We searched databases such as Web of Science, Cochrane Library, PubMed, and Embase for studies published before August 2024 on scar descriptions and psychological states after cardiac surgery. After data extraction and quality assessment, we used RevMan5.4 to analyze the depression and anxiety symptoms of patients after scar formation. Two authors independently performed the focused analyses and reached a final consensus on the included studies, which were subsequently quality checked and risk of bias assessed by a third author. Results Four studies were included in the meta-analysis. All 4 studies used Patient and Observer Scar Assessment Scale (POSAS) to assess scar, and one study also combined Vancouver Scar Scale (VSS) for scar assessment. Meta-analysis results show that Full sternotomy has a smaller scar score than Limited sternotomy (OR = 0.94 [95% confidence interval (CI) 0.28-1.61]; P = 0.005), and there is no significant heterogeneity between the two groups (I2 = 0%). And the postoperative depression score in the Full sternotomy group was higher than that in the Limited sternotomy group (OR = 1.61 [95%CI 0.63-2.60]; P = 0.001), and there was no significant heterogeneity between the two groups (I2 = 0%). However, there was no statistical difference in postoperative anxiety scores between the two groups (OR = 0.70 [95%CI 1.40-2.80]; P = 0.51). There was slight heterogeneity between the two groups (I2 = 58%), so a random effects model was used. Conclusion In conclusion, patients with more severe scarring after cardiac surgery may have more severe depressive symptoms, but adequately powered randomized controlled trials are needed to confirm these results.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.061 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".