Myocardial markers are highly altered by higher rates of fluid removal during hemodialysis
Bibliographic record
Abstract
Abstract Introduction Although hemodialysis is lifesaving in patients with kidney failure extensive interdialytic weight gain (IDWG) between dialyses worsens the prognosis. We recently showed a strong correlation between IDWG and predialytic values of cardiac markers. The aim of the present study was to evaluate if the cardiac markers N‐terminal pro‐B‐type natriuretic peptide (proBNP) and troponin T were influenced by IDWG and speed of fluid removal (ultrafiltration‐rate). Methods Twenty hemodialysis patients performed in total 60 hemodialysis (three each). Predialytic values of proBNP and troponin T and changes from predialysis to 180 min hemodialysis (180–0 min) were compared with the IDWG calculated in percent of body weight. The ultrafiltration‐rate was adjusted (UF‐rateadj) to IDWG: (100 × weight gain between dialysis [kg])/(estimated body dry weight [kg] × length of hemodialysis session [hours]). Results UF‐rateadj correlated (Spearman) with (1) predialytic values of IDWG (r = 0.983, p < 0.001), proBNP (r = 0.443, p < 0.001), and troponin T (r = 0.296, p = 0.025); and (2) differences in proBNP180–0min (r = 0.572, p < 0.001) and troponin T180–0min (r = 0.400, p = 0.002). UF‐ratesadj above a breakpoint of 0.60 caused more release of proBNP180–0min (p = 0.027). Remaining variables in multiple regression analysis with ProBNP180–0min as dependent factor were predialytic proBNP (p < 0.001) and the ultrafiltration‐rate (p < 0.001). Conclusion Higher UF‐rateadj during dialysis was correlated to increased levels of cardiac markers. Data support a UF‐rateadj lower than 0.6 to limit such increase. Further studies may confirm if limited fluid intake and a lower UF‐rateadj should be recommended to prevent cardiac injury during 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 | 0.000 | 0.002 |
| 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".