Depression in older surgical patients: a multicentre prospective longitudinal study
Bibliographic record
Abstract
BACKGROUND: A longitudinal examination of postoperative depression is important for risk assessment. We aimed to explore the prevalence and trajectory of depression in older surgical patients, before surgery, and at 30, 90, and 180 days after surgery, associated risk factors, and clinical outcomes. METHODS: This prospective cohort study assessed 307 male and female surgical patients aged ≥65 yr in two preoperative clinics in Canada. All participants completed an online survey before and after surgery that contained the 15-item Geriatric Depression Scale with a ≥5 cut-off to define depression. We also assessed risk factors and clinical and patient-centred outcomes associated with depression. RESULTS: Preoperative depression was present in 20.2% (95% confidence interval [CI]: 16.1-25.1) of participants and 17.6% had potentially unrecognised depression. Overall, 18.7% (95% CI: 14.1-24.3) reported depression at 180 days after surgery. Participants with preoperative cognitive impairment (odds ratio [OR]: 2.91, 95% CI: 1.29-6.61, P=0.010) and sleep disturbances (OR: 2.91, 95% CI: 1.29-7.07, P=0.013) each had three-fold higher odds of preoperative depression. Those with preoperative functional disability had four-fold higher odds of preoperative depression (OR: 4.15, 95% CI: 1.58-11.54, P=0.005) and six-fold higher odds of depression at 180 days after surgery (OR: 5.91, 95% CI: 1.43-29.07, P=0.019). The depression group had 2.5-fold higher odds for non-home discharge (OR: 2.40, 95% CI: 1.08-5.10, P=0.026). CONCLUSIONS: The prevalence of depression was 20.2% before surgery and 18.7% at 180 days after surgery. Our findings highlight the interconnectedness of mental health with factors such as function, cognition, and sleep.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".