Hydration and Chronic Kidney Disease Progression: A Critical Review of the Evidence
Bibliographic record
Abstract
We performed a comprehensive literature review to examine evidence on the effects of hydration on the kidney. By reducing vasopressin secretion, increasing water intake may have a beneficial effect on renal function in patients with all forms of chronic kidney disease (CKD) and in those at risk of CKD. This potential benefit may be greater when the kidney is still able to concentrate urine (high fluid intake is contraindicated in dialysis-dependent patients). Increasing water intake is a well-accepted method for preventing renal calculi, and current evidence suggests that recurrent dehydration and heat stress from extreme occupational conditions is the most probable cause of an ongoing CKD epidemic in Mesoamerica. In polycystic kidney disease (PKD), increased water intake has been shown to slow renal cyst growth in animals via direct vasopressin suppression, and pharmacologic blockade of renal vasopressin-V2 receptors has been shown to slow cyst growth in patients. However, larger clinical trials are needed to determine if supplemental water can safely slow the loss of kidney function in PKD patients.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".