Spironolactone is effective in treating hypokalemia among peritoneal dialysis patients
Bibliographic record
Abstract
BACKGROUND: Hypokalemia is common in peritoneal dialysis (PD) patients and is associated with increased cardiovascular and all-cause mortality. The management approach for such patients routinely includes spironolactone at our centre. We undertook this study to assess the efficacy of spironolactone for the treatment of hypokalemia in PD patients. METHODS: Retrospective chart review of PD patients at a single centre. Serum potassium was compared prior to initiation of spironolactone and two months afterwards. Indication for spironolactone and changes in blood pressure (BP), weight, and serum creatinine were also recorded. RESULTS: The chart review identified 53 patients who fit our selection criteria. The mean age was 64 +/- 15 years and the majority was treated with continuous cyclic peritoneal dialysis. Serum potassium rose from 3.7 +/- 0.5 to 4.2 +/- 0.5 mmol/L (P<0.0001) after 2 months with a mean dose of spironolactone of 28.5+/-15.2 mg (median dose 25 mg). A significant reduction in systolic BP was observed from 150+/- 18 to 137 +/-24 (P = 0.002); a non- significant reduction in diastolic BP was also observed. The rise in potassium was constant in the range of 0.4 to 0.5 mmol/L regardless of whether spironolactone was initiated for hypokalemia, diuresis, or as an antihypertensive. There was no change in serum creatinine or body weight two months after introduction of spironolactone. CONCLUSIONS: Spironolactone is safe and effective in treating hypokalemia in PD patients. It is also an effective antihypertensive agent and merits further study in the PD population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".