Modifiable variables affecting interdialytic weight gain include dialysis time, frequency, and dialysate sodium
Bibliographic record
Abstract
Interdialytic weight gain (IDWG) is associated with hypertension, left ventricular hypertrophy, and all-cause mortality. Dialysate sodium concentration may cause diffusion gradients with plasma sodium and influence subsequent IDWG. Dialysis time and frequency may also influence the outcomes of this Na(+) gradient; these have been overlooked. Our objective was to identify modifiable factors influencing IDWG. We performed a retrospective multivariable regression analyses of data from 86 home hemodialysis patients treated by hemodialysis modalities differing in frequency and session duration to determine factors involved that predict IDWG. Age, diabetic status, and residual renal function did not correlate with IDWG in the univariable analysis. However, using a combination of backwards selection and Akaike information criterion to build our model, we created an equation that predicted IDWG on the basis of serum albumin, age, patient sex, dialysis frequency, and the diffusive balance of sodium, represented by the product of the duration of dialysis and the patient plasma to dialysate Na(+) gradient. This equation was internally validated using bootstrapping, and externally validated in a temporally distinct patient population. We have created an equation to predict IDWG on the basis of independent factors readily available before a dialysis session. The modifiable factors include dialysis time and frequency, and dialysate sodium. Patient sex, age, and serum albumin are also correlated with IDWG. Further work is required to establish how improvements in IDWG influence cardiovascular and other clinical outcomes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".