Increasing consumption of milk, yoghurt, and cheese in older adults in aged care reduces falls and fractures without adverse effects on serum lipids: a cluster randomised controlled trial
Bibliographic record
Abstract
Correction of dietary calcium and protein undernutrition using milk, yoghurt, and cheese in older adults in aged care homes is associated with reduced fractures and falls(1). As these foods contain potentially atherogenic fats, we aimed to determine whether these dietary changes adversely affect serum lipid profiles. Sixty aged care homes in Australia were randomised to intervention (n = 30 milk, yoghurt, and cheese enriched menu) or control (n = 30 regular menu) for 2 years. A sample of 159 intervention and 86 control residents (median age 87.8 years) had dietary intakes recorded using plate waste analysis and fasting serum lipids measured at baseline and 12 months. Diagnosis of cardiovascular disease and use of relevant medications were determined from medical records. Data were analysed using mixed effects linear regression model adjusting for clustering (aged care home) and other confounders. Intervention increased daily dairy servings from 1.9 ± 1.0 to 3.5 ± 1.4 (p<0.001) while controls continued daily intakes of £ 2 servings daily (1.7 ± 1.0 to 2.0 ± 1.0 (p<0.05). No group differences were observed for serum total cholesterol/high-density lipoprotein-C (TC/HDL-C) ratio, Apoprotein B/Apoprotein A (ApoB/ApoA) ratio, low-density lipoprotein-C (LDL-C), non-HDL-C, or triglycerides (TGs) at baseline and 12 months. Among older adults in aged care homes, correcting insufficiency in the daily intake of calcium and protein using milk, yoghurt and cheese does not alter serum lipid levels, suggesting that this is a suitable intervention for reducing the risk of falls and fractures.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".