Comparison of diet quality tools to assess nutritional adequacy for adults living with kidney disease
Bibliographic record
Abstract
There is no specific diet quality tool recommended for adults living with chronic kidney disease (CKD). Identifying how diet quality tools assess nutritional adequacy and correlate with potassium and phosphorus (nutrients of interest in CKD) is warranted. Our aim was to compare Mediterranean Diet Scores (MDS), Healthy Eating Index (HEI), and Healthy Food Diversity (HFD) to determine their correlation with nutrient intake in adults living with diabetes and CKD. Using data from a longitudinal study of 50 participants with diabetes and CKD, diet quality was assessed at baseline and 1 or more times at annual visits up to 5 years (complete diet records n = 178). Diet quality was investigated for correlation with nutrient intake. Compared with HEI and HFD, MDS was poorly correlated with nutrient intake (all r values <0.40). HFD and HEI were moderately correlated with potassium (r = 0.66, P < 0.01 and r = 0.57, P < 0.01, respectively). HEI was weakly correlated with phosphorus (r = 0.365, P < 0.01). MDS recommends moderation of dairy and meat, this may have specific benefits for CKD as these are both sources of phosphorus, as such high MDS were associated with lower phosphorus intake. This study suggests that development of a renal specific diet quality assessment tool may be useful; however, further studies are needed.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".