SP498INTERNATIONAL VARIABILITY IN THE PREVALENCE OF HYPOKALEMIA AMONG PATIENTS ON PERITONEAL DIALYSIS (PD): RESULTS FROM THE PDOPPS
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Prior studies suggest that hypokalemia is relatively common among PD patients and may contribute to adverse outcomes in this population. However, little is known about hypokalemia and related risk factors internationally. We evaluated prevalence of hypokalemia and selected clinical practices that may impact serum K levels in the international PDOPPS cohort. METHODS: The PDOPPS is a prospective cohort study of PD treatment and outcomes in Australia, Canada, Japan, New Zealand, Thailand, the United Kingdom (UK) and the United States (US). Demographic and clinical data were collected at study enrollment. Hypokalemia was defined as serum potassium <3.5 mEq/L. Logistic generalized estimating equations were used to assess the association between demographic/clinical data and hypokalemia, accounting for within-facility patient clustering. SP498 Figure CONCLUSIONS: Hypokalemia in PD patients is relatively common internationally and very common in Thailand. Poor nutritional status appears to be a major determinant of hypokalemia, while no meaningful differences in PD-specific and other clinical practices were observed. Given the potential risk for adverse events occurring in the presence of hypokalemia, serum K should be closely monitored in PD patients. Low K levels should be addressed with optimized nutritional intake and supplements as needed.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".