Severe vitamin D deficiency in 6 Canadian First Nation formula-fed infants
Bibliographic record
Abstract
BACKGROUND: Rickets was first described in the 17th century and vitamin D deficiency was recognized as the underlying cause in the early 1900s. Despite this long history, vitamin D deficiency remains a significant health concern. Currently, vitamin D supplementation is recommended in Canada for breast fed infants. There are no recommendations for supplementation in formula-fed infants. OBJECTIVE: The objective of this report is to bring attention to the risk of severe vitamin D deficiency in high risk, formula fed infants. DESIGN: A retrospective chart review was used to create this clinical case series. RESULTS: Severe vitamin D deficiency was diagnosed in six formula-fed infants over a two-and-a-half year period. All six infants presented with seizures and they resided in First Nation communities located at latitude 54 in the province of Manitoba. While these infants had several risk factors for vitamin D deficiency, they were all receiving cow's milk based formula supplemented with 400 IU/L of vitamin D. CONCLUSION: This report suggests that current practice with regards to vitamin D supplementation may be inadequate, especially for high-risk infants. Health care professionals providing service to infants in a similar situation should be aware of this preventable condition. Hopefully this would contribute to its prevention, diagnosis and management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 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".