Use of vitamin and mineral supplements in long-term care home residents
Bibliographic record
Abstract
Vitamin-mineral supplementation may offer older adults health and cognition-related benefits but overuse may contribute to polypharmacy. We examined the prevalence of supplement usage in long-term care facility (LTC) residents (≥ 65 years of age). As cognition may be affected by nutrition, we also examined use in those with diagnosis of dementia and those with no dementia diagnosis. The prevalence of supplement usage and overall "pill count" from pharmaceutical use was assessed in 189 LTC residents and a subsample of 56 older adults with dementia diagnosis, respectively. Participants were residing in an LTC facility of a mid-size metropolitan area during 2009. The average use of supplements was 1.0 per day for all residents, with 35% taking vitamin D supplements, 20% multivitamins, and 26% calcium. Supplement use was similar (p ≥ 0.05) for those with dementia diagnosis (53%, average 2.0 per day) and for those without such diagnosis (45%, average 2.2 per day). Usage ranged between 1-6 supplements per day. In both of these groups, ∼73% of users were taking vitamin D. The number of prescribed medications ranged from 4 to 24 (average 10.2) in a subsample of residents whose supplement intake was 0 to 6 (average 2). These findings suggest an overall low rate of supplement use, with no significant differences (p ≥ 0.05) in use between residents with and without dementia diagnosis. However, some residents were at risk for supplement overuse.
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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 | 0.000 | 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 | 0.000 | 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".