Prevalence and correlates of high red blood cell folate concentrations in the Canadian population using 3 proposed cut-offs
Bibliographic record
Abstract
A distinct shift towards higher folate concentrations has emerged in Canada. These higher concentrations have known benefits, including prevention of neural tube defects, but concerns have been raised regarding potential associations with adverse health outcomes. The aim of this research was to propose cut-offs for high red blood cell (RBC) folate concentrations and identify their correlates. RBC folate was measured in a nationally representative cross-sectional sample of Canadians (N = 5248) aged 6 to 79 years. RBC folate concentrations were adjusted from the IMMULITE 2000 immunoassay to a microbiologic assay. The population was characterized at 3 RBC folate cut-offs: 1450 nmol/L, 1800 nmol/L, and 2150 nmol/L. We used t tests to examine differences by age, sex, income, and body mass index (BMI) at each cut-off and logistic regression to explore associations with folic acid supplement intake. The prevalence of high RBC folate was 16%, 6%, and 2% at thresholds of 1450 nmol/L, 1800 nmol/L, and 2150 nmol/L, respectively. Females, those aged 60 to 79 years, and overweight or obese participants had the greatest prevalence of having high RBC folate at each cut-off. Folic acid supplement users were more likely than non-users to have high RBC folate concentrations. Older age, higher BMI, and folic acid supplement use were identified as correlates of high folate status. A high RBC folate concentration cut-off will advance the field towards consistent measurement and reporting of high folate status. This may facilitate future investigation of associations between RBC folate concentrations at the upper end of the distribution and health 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".