The Impact of Depression and Anxiety Comorbidities on Acute Postoperative Pain After <scp>DIEP</scp> Flap Breast Reconstruction
Bibliographic record
Abstract
BACKGROUND: Depression and anxiety have a complex association with opioid dependence, though their impact on acute postoperative pain is unclear. The present study investigated the impact of depression and anxiety on acute postoperative pain and opioid requirements following deep inferior epigastric perforator (DIEP) flap breast reconstruction. METHODS: Patients receiving DIEP flap breast reconstruction were retrospectively identified from 2019 to 2023. Patients were classified into the psychiatric comorbidity (PC) group based on a history of depression or anxiety diagnoses, or the control group. Pain (0-10 Numerical Rating Scale [NRS]) scores and cumulative inpatient opioid requirements (in morphine milli-equivalents [MMEs]) were compared between groups. Linear regression analysis assessed the association of psychiatric comorbidities with pain outcomes. RESULTS: In total, 557 patients were included, of which 11.4% reported a psychiatric history. The PC group was younger (50.0 vs. 52.7 years, p = 0.032) and more frequently reported current marijuana use (15.6% vs. 4.3%, p = 0.001) and former smoking (51.6% vs. 24.5%, p < 0.001). The PC group had higher mean (2.5 vs. 2.0, p < 0.001) and maximum pain scores (8.6 vs. 7.8, p = 0.003) than the control group, which corresponded to higher opioid requirements (256 vs. 223 MMEs, p = 0.041). Psychiatric comorbidities were associated with higher average and maximum pain scores (p < 0.01) while current marijuana use was associated with higher opioid requirements (p = 0.033). CONCLUSION: Patients with depression or anxiety comorbidities tended to have greater acute postoperative pain and opioid consumption. Marijuana use was also more prevalent, potentially exacerbating pain outcomes, and risk of opioid dependence. These findings will inform patient discussions and targeted interventions to mitigate opioid misuse.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".