The Effects of Resistant Starch Consumption in Adult Patients with Chronic Kidney Disease: A Systematic Review Protocol
Bibliographic record
Abstract
Abstract Background: Dietary modifications represent an important intervention to reduce the risk of chronic disease. Appropriate dietary changes can be simple to implement and are usually suggested as first line recommendations for many types of chronic disease management including diseases like hypertension and diabetes. Resistant starch is a non-digestible carbohydrate which passes through untouched to the colon where it becomes a digestible substrate for beneficial colonic bacteria. The normal gut flora becomes damaged in patients with chronic kidney disease with an additional buildup of uremic toxins and resulting intra-renal inflammation. Resistant starch supplementation has been studied for its effects on reducing harmful metabolite buildup through a restoration of normal gut flora. The following systematic review aims to compile the evidence of resistant starch use in adult patients with chronic kidney disease to answer whether or not the dietary intervention can reduce the progression of renal disease. Methods: We will perform a literature search with a knowledge synthesis librarian including the following databases: MEDLINE, Cochrane Central, Embase, CINAHL, and Web of Science. Collected data will be extracted and organized in MS Excel 2019. The extraction of the data will include study details, study population details, intervention details, and outcome details.Discussion: Results from this systematic review may help to determine a simple and effective method for prolonging the need for renal replacement therapy through resistant starch supplementation. Lack of evidence may highlight the need for additional trials or new interventions. PROSPERO ID: Currently being assessed, ID: 203138.
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 | 0.038 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.052 | 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".