Vitamin B<sub>12</sub> status in older adults living in Ontario long-term care homes: prevalence and incidence of deficiency with supplementation as a protective factor
Bibliographic record
Abstract
Vitamin B12 (B12) deficiency, although treatable, impacts up to 43% of community-living older adults; long-term care (LTC) residents may be at greater risk. Recommendations for screening require further evidence on prevalence and incidence in LTC. Small, ungeneralizable samples provide a limited perspective on these issues. The purposes of this study were to report prevalence of B12 deficiency at admission to LTC, incidence 1 year post-admission, and identify subgroups with differential risk. This multi-site (8), retrospective prevalence study used random proportionate sampling of resident charts (n = 412). Data at admission extracted included demographics, B12 status, B12 supplementation, medications, diagnoses, functional independence, cognitive performance, and nutrition. Prevalence at admission of B12 deficiency (<156 pmol/L) was 13.8%; 47.6% had normal B12 (>300 pmol/L). One year post-admission incidence was 4%. Better B12 status was significantly associated with supplementation use prior to LTC admission. Other characteristics were not associated with status. This work provides a better estimate of B12 deficiency prevalence than previously available for LTC, upon which to base protocols and policy. Prospective studies are needed to establish treatment efficacy and effect on health related outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".