An Observational Study on Depressive Illness in Hemodialysis Recipients: A Review on its Association and Prognostication
Bibliographic record
Abstract
Major Depressive Disorder (MDD) is one of the most common psychiatric illnesses. The effect of depression on one’s physical health is well-known, which can include anything from weight gain or loss to chronic illnesses such as heart disease, kidney or gastrointestinal problems. Provided the increasing prevalence of patients suffering from End Stage Renal Disease (ESRD) and receiving hemodialysis treatment, it is important to investigate how MDD affects the outcome of their treatment. The incidence of depression in dialysis patients ranges from 10% to 66% in various studies, with prevalence reaching as high as 100%. The purpose of this article is to find the prevalence and severity of major depressive disorder in dialysis patients as well as to describe the possible pathways MDD worsens the dialysis outcome. Our study population consisted of 51 End Stage Renal Disease (ESRD) patients sampled from the Department of Nephrology at BIRDEM General Hospital. Neurocognitive, physical symptoms, the severity of MDD and presence of comorbid conditions including diabetics and hypertension, were measured in our study. The ESRD patient sample consisted 47.7% moderately depressed patients, 34% severely depressed, 11.4% mild and 6.8% with minimal MDD patients. Analogous to Hypertension and Diabetic patients with depression, the number of Chronic Kidney Disease (CKD) patients with mostly moderate severe depression increased with the duration of the disease. The article explains a myriad of biologic, behavioral, genetic and social factors underlying the association of depression and adverse medical outcomes in patients with CKD and ESRD. Moreover, neuroimaging data is required for further discussion on relationship between Depression and CKD. The implication of this study is to emphasize the importance of dialysis patients’ overall health and to serve as a pretext for further research into depression in dialysis patients. Bangladesh Crit Care J March 2020; 8(1): 41-47
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".