Mood disorders in patients with chronic kidney disease: Significance, etiology and prevalence of depression
Bibliographic record
Abstract
Due to the rapidly increasing number of end-stage renal disease patients and the high costs of their treatment, all the aspects of kidney disease that may significantly affect clinical outcome (quality of life mortality) deserve increasing attention. It has been established and accepted that in addition to clinical/somatic factors, also psycho-social factors, including depression, may have a significant impact on the clinical outcome of chronic diseases. Depression is considered to be one of the most prevalent mental health problems in patients with chronic kidney disease. In spite of this fact, there are only few studies on the prevalence, diagnosis and treatment of depression in this population using accurate, well defined diagnostic criteria and appropriate epidemiologic methods. In the last decades we have experienced a significant improvement in the quality and effectiveness of the therapeutic options for chronic kidney disease, but mortality is still very high in this population. Our review provides an overview of the literature regarding the prevalence and etiology of depression, and calls the attention to the interrelation among depression, quality of life and mortality. The second part of our paper to be published later will survey the specific diagnostic and therapeutic features of depression in chronic kidney disease patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".