Standardizing vitamin D supplementation to minimize deficiency in children with intestinal failure
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it