Optimal fluid control can normalize cardiovascular risk markers and limit left ventricular hypertrophy in thrice weekly dialysis patients
Bibliographic record
Abstract
Increased hemodialysis frequency can make fluid overload easier to treat, although most patients are still treated thrice weekly. Chronic fluid overload is associated with left ventricular hypertrophy and elevated serum cardiac biomarkers, recognized as mortality risk factors. Serum cardiac troponin T (cTnT), N-terminal prohormone brain natriuretic peptide (NT-proBNP), left ventricular mass index by cardiac magnetic imaging, and ambulatory blood pressure was measured in 30 thrice weekly hemodiafiltration patients. Time-averaged fluid overload (TAFO) was quantified by bioimpedance spectroscopy. In the study group, left ventricular hypertrophy was found to be 26% by cardiac magnetic resonance. Ambulatory blood pressure was 130 mmHg (112-151) requiring a low equivalent dose of medication of 0.25 units (0-1). Significantly, lower levels of left ventricular mass index (P < 0.05) were associated in those patients with TAFO <1 L or NT-proBNP <1200 pg/mL or cTnT <0.1 ug/L. In the subgroups, 16 patients had normal cTnT (<0.03 ug/L), 16 patients had NT-proBNP <400 pg/mL, and 20 patients had TAFO <1 L. Nine patients had both cTnT <0.03 ug/L and NT-proBNP <400 pg/mL. Normally hydrated thrice-weekly hemodiafiltration patients can have cardiac biomarker and TAFO levels indistinguishable from the normal healthy population. Obtaining TAFO by bioimpedance monitoring can offer a practical alternative to serum cardiac biomarkers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.000 | 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 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".