A Meta-analysis on the Relationship between Different Dialysis Modalities and Depression in End-stage Renal Disease Patients
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to assess the relationship between different dialysis modalities and depression in end-stage renal disease (ESRD) patients. METHODS: We searched through the PsycINFO, PubMed, Cochrane Library, EMBASE, and CNKI for all related studies from 1 January 1990 through 30 June 2019 without restriction on language. We selected papers that compared depression levels among patients undergoing hemodialysis and peritoneal dialysis. Two authors independently selected studies, evaluated the quality of included studies, and extracted data according to Newcastle-Ottawa Scale (NOS). A discussion with a third author checked any disagreement to minimize the publication bias. PRISMA guidelines were used as the standards of reporting (PRISMA registration ID is 239172). RESULTS: There was insufficient evidence to prove the relationship between different dialysis modalities and depression (OR: 2.37, 95% CI: 0.88-6.40). We also found no statistical significance between the mean difference of depression level and dialysis modalities (Std mean difference=0.69, 95% CI: -2.09-3.46). CONCLUSION: The available limited, deficient quality evidence assessed by ROBINS-I does not support an association between depression and dialysis modalities among ESRD patients. Further studies that provide data for different sex and age groups are needed to clarify whether a subgroup of dialysis modalities has a different risk of depression.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".