Screening for depression: only one piece of the puzzle
Bibliographic record
Abstract
In this issue of NDT, van den Beukel et al. from the Netherlands suggest that a 5-item survey questionnaire might be used to replace the Beck Depression Index to screen patients with chronic kidney disease (CKD) for depression. The nephrology community is at a tipping point in terms of the assessment of outcomes, especially among patients on dialysis. Indeed, the entire healthcare community has begun to shift its focus to patient-reported outcomes (PROs), including quality of life, patient satisfaction and the psychosocial determinants of health. Beyond depression, there are a myriad of aspects of psychological distress that include anxiety, worrying, fear of progression of kidney disease and the fear of the future in general, death and dying, hopelessness, questions around the meaning of life and the experience of recurrent psychological and physical trauma through the CKD trajectory. We encourage the community and its researchers to embrace and research PROs, with the aim to create a holistic, patient-centered model of care for patients at all stages of CKD, including those on chronic dialysis and after transplantation, keeping the whole person-and their families-in mind.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.026 | 0.033 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".