Diet quality is associated with malnutrition and low calf circumference in Canadian long-term care residents
Bibliographic record
Abstract
Abstract Background Older adults living in long-term care (LTC) are nutritionally vulnerable. The purpose of this study was to determine diet quality of Canadian LTC residents and its association with malnutrition and low calf circumference. Methods A cross-sectional study was undertaken in 32 LTC homes across four Canadian provinces. Nutrient adequacy ratios (NARs) were calculated for seventeen nutrients; mean adequacy ratio (MAR) was calculated to describe overall diet quality. Malnutrition risk was assessed with the Mini Nutritional Assessment-Short Form (MNA-SF) and diagnosis of protein/energy malnutrition with the Patient-Generated Subjective Global Assessment (PG-SGA). Calf circumference (CC) was also assessed. Linear and logistic regressions for these outcomes with diet quality as the predictor were conducted adjusting for covariates. Results Average MNA-SF score was 10.7 ± 2.5. Residents (43.5%) had mild/moderate to severe malnutrition based on the PG-SGA and 32.6% had a CC of < 31 cm. Mean MAR score was 0.79 ± 0.09 with significant differences between those requiring eating assistance (0.77 ± 0.11) and those that did not require assistance (0.80 ± 0.07) (p < .05). MAR score was significantly associated with malnutrition in fully adjusted models: MNA-SF scores [β = 5.34, 95% Confidence interval (CI) (2.81, 7.85)] and PG-SGA [Odds ratio (OR) = 0.49, 95% CI (0.38, 0.64)]. Those who had better diet quality were more likely to be well nourished or not at risk. Although several individual nutrients were associated with low CC (< 31 cm), there was no association between overall diet quality (MAR) and low CC. Conclusions Diet quality is associated with malnutrition and individual nutrients (NARs) with a low CC. In addition to calories and protein, nutrient dense diets that promote adequate micronutrient intake are required in LTC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".