Variability in Cardiac Biomarkers during Hemodialysis: A Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The effect of hemodialysis on cardiac biomarkers is unclear. We sought to evaluate the degree and causes of intradialytic variability of high sensitivity troponin I (hs-TnI), galectin-3 (gal-3), and heart-type fatty acid binding protein (hFABP). METHODS: hs-TnI, gal-3, and hFABP were prospectively measured pre-dialysis and post-dialysis for 1 week every month for 6 months in 178 prevalent adult hemodialysis patients at a single center in Hamilton, Canada. The degree of change from pre-dialysis to post-dialysis for each cardiac biomarker was estimated with multilevel linear regression models. RESULTS: The median change in the concentration of hs-TnI during hemodialysis was -1 ng/L (interquartile range [IQR] -1 to 2 ng/L) while gal-3 and hFABP changed by -36.3 ng/mL (IQR -27.7 to -46.8 ng/mL) and -19.41 ng/mL (IQR -13.61 to -26.87 ng/mL), respectively. The median (IQR) percentage intradialytic changes for hs-TnI, gal-3, and hFABP were 2.6% (-4.4% to 12.5%), -59.8% (-54.7% to -64.8%) and -35.3% (-28.4% to -42.1%), respectively. Ultrafiltration was associated with an increase in concentration of hs-TnI, gal-3, and hFABP (mean 0.99 ng/L, 1.05 ng/mL, and 1.9 ng/mL per L ultrafiltration, respectively, P < 0.001). Both gal-3 and hFABP concentrations decreased in association with the volume of blood processed (P < 0.001) and with hemodialysis treatment time (P = 0.02 and P = 0.04) while hs-TnI concentration decreased only in association with hemodialysis treatment time (P < 0.001). CONCLUSIONS: Ultrafiltration volume and hemodialysis treatment time influenced hs-TnI, gal-3, and hFABP concentrations during hemodialysis and should be considered when interpreting their measurement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".