Standardizing vitamin D supplementation to minimize deficiency in children with intestinal failure
Bibliographic record
Abstract
BACKGROUND: Vitamin D deficiency is present in 40%-70% of children with intestinal failure (IF), yet there are no published guidelines for repleting and maintaining vitamin D levels in this population. The purpose of this study is to evaluate the efficacy of a standardized vitamin D algorithm in reducing the incidence of deficiency. METHODS: ) measurement. Vitamin D levels were compared prealgorithm (2014-2016) and during active-algorithm use (2018-2020). Vitamin D levels were classified as severe deficiency (<12.5 nmol per L), mild deficiency (12.5-39 nmol/L), insufficiency (40-74 nmol/L), optimal (75-224 nmol/L), or toxicity (>225 nmol/L). Descriptive and comparative statistics were calculated using a linear mixed-effects model, with P < 0.05 considered significant. RESULTS: Twenty-eight children with IF were enrolled, which included 157 vitamin D measurements (58 in the prealgorithm group and 98 in the active-algorithm group). Algorithm compliance was 4% in the prealgorithm group and 61% in the active-algorithm group. Active-algorithm patients had improved vitamin D levels in all categories compared with those of prealgorithm patients (mild deficiency: 8% vs 9%; insufficiency: 41% vs 72%; optimal: 50% vs 19%). Algorithm use was found to have a statistically significant effect on serum vitamin D levels (β = 21.58; 95% confidence interval, 14.11-29.05; P < 0.005). CONCLUSIONS: Children with IF are at high risk for vitamin D deficiency. Use of a standardized vitamin D supplementation algorithm was associated with increased serum vitamin D levels.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| 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".