Does cooled dialysate still have a role in reducing intradialytic stress? Implications of the MyTEMP trial
Bibliographic record
Abstract
PURPOSE OF REVIEW: There is an excess of cardiovascular morbidity and mortality in the maintenance haemodialysis population. Targeting traditional risk factors (e.g. hypercholesterolaemia) do not improve cardiovascular outcomes. Repeated myocardial stunning during haemodialysis is an important nontraditional risk, resulting in pathological cardiac remodelling and fibrosis. This review explores dialysate cooling as a management strategy to promote haemodynamic stability, reduce myocardial injury, and improve cardiovascular disease outcomes for individuals receiving maintenance haemodialysis. RECENT FINDINGS: Observational data and small interventional studies demonstrate dialysate cooling has the potential to reduce end-organ damage and provide cardioprotection, renal protection and neuroprotection compared with standard care. These data are limited by the small sample sizes, short follow-up times and lack of long-term patient important outcomes. The MyTEMP study, a multicentre pragmatic randomized controlled trial, demonstrated cooled dialysate (0.5°C below body temperature) vs. standard care did not improve cardiovascular outcomes for prevalent haemodialysis patients. SUMMARY: Dialysate cooling has been widely adopted into routine clinical practice; the MyTEMP study challenges the unit-level approach to implementing dialysate cooling. Due to methodological limitations, the absence of other important patient outcome measures, and lack of granularity of patient-level data, dialysate cooling should not be hastily removed from all dialysis care and warrants further research.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".