Increasing Water Intake in Chronic Kidney Disease: Why? Safe? Possible?
Bibliographic record
Abstract
Increased water intake may slow the progression of chronic kidney disease by lowering vasopressin levels. Prior to initiating a large randomized controlled trial on the effect of increased water intake on renal decline, we conducted a six-week pilot study to examine the safety and feasibility of asking adults with chronic kidney disease to increase their water intake. We randomly assigned 29 patients to either a hydration or a control group. The hydration group was asked to increase water intake by 1 to 1.5 l/day relative to their weight, gender, and 24 h urine osmolality, in addition to usual consumed beverages; the control group was asked to continue with usual fluid intake. After six weeks, the change in urine volume was significantly different between groups (0.9 l/day; p = 0.002) with no change in serum sodium and no serious adverse effects. Similarly, preliminary results of our large clinical trial of the same intervention (489 patients enrolled to date) demonstrated a significant separation between groups on 24 h urine volume (at 12 months the mean difference between groups was 1.2 l/day; p < 0.001) with no serious adverse effects. Serum sodium has remained stable in both groups over follow-up. To our knowledge, this trial is currently the largest of its kind to date; the significant separation between groups with respect to urine volume indicates that we will have scientifically reliable data on the effect of increased fluid intake on renal decline. The analysis of primary and secondary outcomes will be conducted at the conclusion of follow-up in July 2016.
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".