Low micronutrient intake in nursing home residents, a cross-sectional study
Bibliographic record
Abstract
Low intake of micronutrients is associated with health-related problems in nursing home residents. As their food intake is generally low, it is expected that their micronutrient intake will be low as well. The nutrient intake of 189 residents (mean age 85.0 years (SD: 7.4)) in five different Dutch nursing homes was measured based on 3-day direct observations of intake. Micronutrient intake, without supplementation, was calculated using the Dutch food composition table, and SPADE software was used to model habitual intake. Intake was compared to the estimated average requirement (EAR) or adequate intake (AI) as described in the Dutch dietary reference values. A low intake was defined as >10% not meeting the EAR or when the P50 (median) intake was below the AI. Vitamin A, thiamin, riboflavin, niacin, B6, folate, B12, C, D, E, copper, iron, zinc, calcium, iodine, magnesium, phosphorus, potassium, and selenium were investigated. Our data showed that vitamin and mineral intake was low for most assessed nutrients. An AI was only seen for vitamin B12 (men only), iodine (men only), and phosphorus. A total of 50% of the population had an intake below the EAR for riboflavin, vit B6, folate, and vitamin D. For reference values expressed in AI, P50 intake of vitamin E, calcium, iodine, magnesium, potassium, and selenium was below the AI. To conclude: micronutrient intake in nursing home residents is far too low in most of the nursing home population. A "food-first" approach could increase dietary intake, but supplements could be considered if the "food-first" approach is not successful.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".