Vitamin D levels in peanut allergic children
Bibliographic record
Abstract
The prevalence of peanut allergy is increasing. The reasons for this are not entirely known. A factor may be vitamin D (Vit D). This study was performed in a referral allergist’s office in Ontario. Prospectively, all patients (<18 years old) with peanut allergy who were tested for peanut specific IgE (PN IgE) also had Vit D measured. All measurements were done between December 2010 and May 2011. The Vit D measure was 25-hydroxy vitamin D. Patients were divided into three groups: deficient (less than 25 nmol/L), insufficient (25-75 nmol/L) and sufficient (75-250 nmol/L). Vit D levels were compared to PN IgE, sex, age, body mass index (BMI) and other allergies. Fifty peanut allergic patients were included. The mean Vit D level of the patients was 73.8 nmol/L and the 95% confidence interval was 69.6 - 75.7 nmol/L. One patient (2%) had deficient and thirty-one (62%) of the patients had insufficient Vit D levels. Nineteen (38%) had Vit D levels in the sufficient range. There was no correlation between Vit D levels and PN IgE or BMI. Generalized linear modeling showed that vit D levels were predicted by age and sex (p=0.04 & p=0.002, respectively). Two percent of our patients had deficient Vit D levels while 62% of our patients had insufficient Vit D levels. These levels were statistically associated with age and sex. Insufficiency of Vit D may play a role in peanut allergy.
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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 | 0.000 | 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".