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Record W1984773695 · doi:10.1139/h11-141

Use of vitamin and mineral supplements in long-term care home residents

2012· article· en· W1984773695 on OpenAlexafffundvenue
Navita Viveky, Lynda Toffelmire, Lilian Thorpe, Jennifer Billinsky, Jane Alcorn, Thomas Hadjistavropoulos, Susan J. Whiting

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2012
Typearticle
Languageen
FieldHealth Professions
TopicTherapeutic Uses of Natural Elements
Canadian institutionsUniversity of ReginaUniversity of Saskatchewan
FundersSaskatchewan Health Research Foundation
KeywordsTerm (time)Long-term careMedicineVitaminGerontologyEnvironmental healthNursingEndocrinology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.586

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.045
GPT teacher head0.372
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations21
Published2012
Admission routes3
Has abstractyes

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