Bibliographic record
Abstract
INTRODUCTION: Continued progression of kidney disease will lead to renal function too low to sustain healthy life. In developed countries, such people will be offered renal replacement therapy in the form of dialysis or renal transplantation. Requirement for dialysis or transplantation is termed end-stage renal disease (ESRD). METHODS AND OUTCOMES: We conducted a systematic review, aiming to answer the following clinical questions: What are the effects of a low-sodium diet to reduce progression rate of chronic kidney disease? What are the effects of a low-protein diet to reduce progression rate of chronic kidney disease? We searched: Medline, Embase, The Cochrane Library, and other important databases up to September 2014 (Clinical Evidence overviews are updated periodically; please check our website for the most up-to-date version of this overview). RESULTS: We found seven studies that met our inclusion criteria. We performed a GRADE evaluation of the quality of evidence for interventions. CONCLUSIONS: In this systematic overview we present information relating to the effectiveness and safety of the following interventions: low-protein diet versus control, different low-protein diets versus each other (low-protein diet versus very low-protein diet), low-sodium diet versus control, different low-sodium diets versus each other.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.038 | 0.004 |
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".