Factors Contributing to the Burden of Depression Amongst Patients Receiving Hemodialysis at Public and Private Dialysis Centres
Bibliographic record
Abstract
Background: Chronic kidney disease poses significant morbidity on patients and subjects them to stressors in financial, occupational, and social aspects, making them vulnerable to mental health problems. We estimated the prevalence of depression in CKD patients undergoing maintenance hemodialysis (MHD) and evaluated the factors affecting it. Materials and Methods: This cross-sectional survey included 282 patients from four Apex Kidney Care centers, Mumbai. Their mental health was assessed using PHQ-9 survey, a validated questionnaire for identifying depression. Categorical variables were compared using the Chi square test and continuous variables with the Mann Whitney U test. Logistic regression was used for multivariate analysis and odds ratios were calculated. Results: Females constituted 36.52% of the study population. There was an equal distribution of patients from charitable centers (142 patients) and private centers (140 patients). The current analysis focused on those patients (n = 60) with significant depression i.e. a PHQ-9 score of 10 or greater, and these were compared to the rest of patients (n = 222). In logistic regression, female gender (p = 0.002), catheter as access (p = 0.025), stress of food restriction (p < 0.0001) showed statistically significant positive association, whereas being employed (p = 0.022) showed statistically significant negative association with depression. The distribution of patients with significant depression in both public (21.10%) and private (21.40%) centers was equal. Conclusion: The prevalence of depression in MHD patients is substantial. Employment status, catheter access, and food restrictions are the modifiable factors influencing mental health. A focused approach on maximizing arterio-venous fistula creation, diet counseling, employment friendly shift adjustments, and mental health counseling can help mitigate this challenge.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".