Frequent Nocturnal Hemodialysis Associates with Improvement of Prolonged QTc Intervals
Bibliographic record
Abstract
BACKGROUND/AIMS: Sudden cardiac death remains the leading cause of death in hemodialysis (HD) patients. Prolongation of QTc intervals (as measured by the tangent method) increases sudden cardiac death risk in populations without kidney disease. METHODS: We performed a retrospective electrocardiograph (ECG) and chart review of HD patients. Our objectives were (1) to establish the effect of one of four different dialysis modalities on interdialytic QTc intervals, (2) to determine the effect of dialysis frequency and time on QTc interval and on the prevalence of borderline or prolonged QTc intervals, and (3) to determine if changes in QTc interval were simultaneous to changes in electrocardiographic left ventricular mass. RESULTS: Frequent nocturnal HD was associated with a decrease in QTc interval for all patients (from 436.5 to 421.3 ms, p = 0.0187) and for patients who initiated dialysis with prolonged QTc (468.2 to 438.2 ms, p = 0.0134). This change happened before changes in left ventricular mass were evident. Dialysis duration predicted a decrease in QTc better than dialysis frequency (R(2) 6.50 vs. 3.00%, p = 0.023 vs. 0.102). Prevalence of borderline or prolonged QTc increased in patients dialyzed <4 h/session (12/39 to 22/39, p = 0.039). CONCLUSIONS: Frequent nocturnal HD may be the ideal modality to initiate HD in end-stage kidney disease patients with prolonged QTc.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".