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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".