Screening for depression in chronic hemodialysis patients: Comparison of the Beck Depression Inventory, primary nurse, and nephrology team
Bibliographic record
Abstract
Depression in patients with end-state renal disease (ESRD) is both underdiagnosed and treated, which may contribute to an increase in morbidity and mortality. Efforts aimed at screening, diagnosing, and treating depression could potentially modify outcomes in this population. The purpose of this study was to compare the prevalence of depression, as measured by the Beck Depression Inventory (BDI-II), the primary nurse, and nephrology team, among a cohort of patients receiving chronic hemodialysis (HD). A secondary objective was to identify patient variables associated with depression. Patients were screened for depression at the same time point, using the BDI-II, the primary nurse and the nephrology team. Depression was defined as a BDI-II score > or =14. Agreement between the BDI-II score, nurse, and nephrology team assessment of depression was compared using a kappa score and receiver-operating characteristic (ROC) curves were generated. One hundred and twenty-four of an eligible 154 patients completed the study. Depression as measured by a BDI-II> or =14, the nurse and the team was diagnosed in 38.7%, 41.9%, and 24.2% of patients, respectively. With the BDI-II as the gold standard, the nurses' diagnosis of depression had an agreement of 74.6% vs. only 24.2% agreement with the nephrology team. A previous history of malignancy was the only variable associated with the diagnosis of depression. Depression is common among patients on HD, supporting the need for a routine depression-screening program. The primary dialysis nurse is in a key position to identify patients with depression and should be considered as an integral part of the nephrology team.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".