Identifying patients at risk for depression after radical cystectomy
Bibliographic record
Abstract
INTRODUCTION: We aimed to assess rates of depression in patients with bladder cancer undergoing radical cystectomy and identify its predictors. METHODS: Depressive symptoms in 42 consecutive patients were evaluated using the Beck's Depression Inventory (BDI) on the day prior to surgery, postoperative day (POD) 6, six weeks after surgery, and 12-18 months postoperatively. RESULTS: Fifteen patients (36%) presented with BDI scores ≥10 before the operation; this rate increased to 64% on POD 6 and 69% at six weeks post-surgery. Depression score rose from a preoperative median of 7 to 11 on POD 6 (p=0.003) and to 15 at six weeks after surgery (p=0.001). Patients who arrived with BDI score of <10 had a higher increase in the BDI at six weeks compared to patients with depressive symptoms prior to surgery (average increase 9.8 vs. 0.8, p<0.01). Age, gender, type of diversion, and complications were not associated with depression at presentation or progression of depression. Patients who did not receive neoadjuvant chemotherapy tended to be at increased risk for depression progression (57.1% vs. 14.3%, p=0.093). Twenty-four patients completed a fourth questionnaire 12-18 months postoperatively. Median BDI score was 8; three patients with disease recurrence had a higher increase in the BDI score (average 12.7 vs. -5.2, p<0.01). CONCLUSIONS: Depression among patients facing cystectomy is high and postoperative progression is substantial. Patients without depressive symptoms preoperatively are at increased risk of developing postoperative depression. After 12-18 months, the most influential risk factor for depression is recurrence. These findings highlight the need to consider interventions in selected patients.
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 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 | 0.001 |
| 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".