Pharmacological treatment for mental health illnesses in adults receiving dialysis: A scoping review
Bibliographic record
Abstract
BACKGROUND: Pharmacologic management of mental health illnesses in patients receiving dialysis is complex and lacking data. OBJECTIVE: Our objective was to synthesize published data for the treatment of depression, bipolar and related disorders, schizophrenia or psychotic disorders, and anxiety disorders in adults receiving hemodialysis or peritoneal dialysis. METHODS: We undertook a scoping review, searching the following databases: Medline, Embase, CINAHL, PsycINFO, Cochrane Library, Scopus, and Web of Science. Data on patients who received only short-term dialysis, a kidney transplant, or non-pharmacologic treatments were excluded. RESULTS: Seventy-three articles were included: 41 focused on depression, 16 on bipolar disorder, 13 on schizophrenia and psychotic disorders, 1 on anxiety disorders, and 2 addressing multiple mental health illnesses. The majority of depression studies reported on selective serotonin reuptake inhibitors (SSRIs) as a treatment. Sertraline had the most supporting data with use of doses from 25 to 200 mg daily. Among the remaining SSRIs, escitalopram, citalopram, and fluoxetine were studied in controlled trials, whereas paroxetine and fluvoxamine were described in smaller reports and observational trials. There are limited published data on other classes of antidepressants and on pharmacological management of anxiety. Data on treatment for patients with bipolar disorder or schizophrenia and related disorders are limited to case reports. CONCLUSION: Over half of the studies included were case reports, thus limiting conclusions. More robust data are required to establish effect sizes of pharmacological treatments prior to providing specific recommendations for their use in treating mental health illnesses in patients receiving dialysis.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".