Examining the association between vitamin B12 deficiency and dementia in high-risk hospitalized patients
Bibliographic record
Abstract
Objectives To explore the association between vitamin B12 deficiency and dementia in patients at high risk for vitamin B12 deficiency . Design Chart review. Setting Emergency, critical care/trauma, neurology , medicine, and rehabilitation units of two hospitals in Southwestern Ontario, Canada. Participants Adult patients (n = 666) admitted from 2010 to 2012. Data collection included: reason for admission, gender, age, clinical signs and symptoms of B12 deficiency, serum B12 concentration, and B12 supplementation. Patients with dementia were identified based on their medication profile and medical history . Vitamin B12 deficiency (pmol/L) was defined as serum B12 concentration <148; marginal deficiency: ≥148–220 and adequate >220. Comparisons between B12-deficient patients with and without dementia were examined using parametric and non-parametric tests. Results Serum B12 values were available for 60% (399/666) of the patients, of whom 4% (16/399) were B12-deficient and 14% (57/399) were marginally deficient. Patients with dementia were not more likely to be B12-deficient or marginally deficient [21% (26/121)] compared to those with no dementia [17% (47/278), p=0.27)]. Based on documentation, 34% (25/73) of the B12-deficient and marginally-deficient patients did not receive B12 supplementation, of whom 40% (10/25) had dementia. Conclusion In this sample of patients, there was no association between B12 deficiency and dementia. However, appropriate B12 screening protocols are necessary for high risk patient to identify deficiency and then receive B12 supplementation as needed.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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".