Risk of major depression in patients with chronic renal failure on different treatment modalities: A matched‐cohort and population‐based study in <scp>T</scp>aiwan
Bibliographic record
Abstract
The influence of different treatment modalities on the risk of developing major depression in patients with chronic renal failure (CRF) is not well understood. We aimed to explore the incidence of major depression among patients with CRF who were on different dialysis modalities, who had received renal transplantation (RT), and those who had not yet received any of the aforementioned renal replacement therapies. We conducted a population-based retrospective cohort study using a national health insurance research database. This study investigated 89,336 study controls, 17,889 patients with chronic kidney disease on conservative treatment, 3823 patients on hemodialysis (HD), 351 patients on peritoneal dialysis (PD), and 322 patients who had RT. We followed all individuals until the occurrence of major depression or the date of loss to follow-up. The PD group had the highest risk (hazard ratio [HR] 2.43; 95% confidence interval [CI] 1.26-4.69), whereas the RT group had the lowest risk (HR 0.18; 95% CI 0.03-1.29) of developing major depression compared with the control group. Patients initiated on PD had a higher risk of developing major depression than patients initiated on HD (pairwise comparison: HR 2.20; 95% CI 1.09-4.46). Different treatment modalities are associated with different risks of developing major depression in patients with CRF. Among renal replacement therapies, patients who have had RT have the lowest risk of developing major depression. Patients who initiate renal therapy on PD may have a higher risk of major depression compared with patients who initiate renal therapy on HD.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".