Preoperative Depression and Anxiety Impact on Inpatient Surgery Outcomes
Bibliographic record
Abstract
Objectives: To determine the association of preoperative mood symptoms and postoperative adverse outcomes; to explore sex-specific differences. Background: Depression and anxiety can increase postoperative mortality. Psychological stress is associated with a chronic inflammatory response unfavorable to postsurgical healing. Methods: Prospective cohort study. Patients were recruited from surgical preadmission clinics at a university hospital. Preoperative depression and anxiety were measured via the Beck Depression and Beck Anxiety Inventories (BDI-II and BAI). Our primary outcome was a composite of postoperative complications, extended length of stay (ELOS) and early readmission. Associated variables included demographics, preoperative pain, pain tolerance/catastrophizing, coping mechanisms, postoperative pain, and opioid use. We adjusted for age, comorbidities, and surgical specialty. Results: Of 1061 recruited patients (ten surgical specialties, 2015–2020), 455 males and 486 females had preoperative and postoperative data available. Mean age was 62.9 (range 20.2–96.2). At baseline, 9.3% of patients had moderate or severe depression; 7.4% had moderate or severe anxiety. Females were more likely to be moderately or severely depressed (11% vs 7%, P = 0.036) and moderately or severely anxious (9% vs 6%, P = 0.034). Females had significantly fewer reported comorbidities and lower American Society of Anesthesiologists category ( P < 0.001). Increasing BDI-II and BAI scores significantly increased likelihood of postoperative complications, ELOS, and/or hospital readmission in females (adjusted odds ratio [aOR] = 2.57 for BDI-II 1-19 vs 0, P = 0.041; aOR = 4.48 for BDI-II > 19 vs 0, P = 0.008; aOR = 1.54 for BAI ≤ 6 vs >6, P = 0.038) but not in males. Mood symptoms did not influence postoperative pain or opioid use. Conclusion: Preoperative depression and anxiety negatively impact surgical outcomes in female patients undergoing major 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 | 0.000 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".