Monitoring of 25-OH Vitamin D Levels in Children With Cystic Fibrosis
Bibliographic record
Abstract
BACKGROUND: Patients with cystic fibrosis are at risk for malabsorption of fat-soluble vitamins, and those with low 25-OH vitamin D levels have a higher risk of low bone mineral density and long-term skeletal complications. It is currently recommended that vitamins A and E be monitored yearly; however, no recommendations exist for 25-OH vitamin D. Because all three vitamins are fat-soluble, the hypothesis in the current study was that low levels of vitamins A and E could identify patients at risk for low 25-OH vitamin D, so that 25-OH vitamin D measurements could be obtained in only selected circumstances. METHODS: Forty (21 girls) patients with CF, age 10.5 +/- 3.9 (SD) years, were assessed in a cross-sectional survey for ideal weight for height (percentage of predicted), spirometry (percentage of predicted FEV1, 33/40 patients), and serum levels of vitamins A, E, 25-OH vitamin D, and cholesterol (37/40 patients). RESULTS: Nine (22.5%) of 40 patients were malnourished (percentage of predicted ideal weight for height <85%), 7 (21.2%) of 33 had moderate to severe lung disease (FEV1 <60%), 4 (10%) of 40 had low levels of vitamin A, 3 (7.5%) of 40 had low vitamin E levels, 4 (10.8%) of 37 low vitamin E/cholesterol levels, and 4 (10%) of 40 had marginal or low levels of 25-OH vitamin D (<40 mmol/l). The patients with low 25-OH vitamin D were older, with no child < 12 years of age having a 25-OH vitamin D level less than 40 mmol/l. They also had lower vitamin E and vitamin E/cholesterol levels than those with normal 25-OH vitamin D levels. The groups did not differ in percentage of predicted ideal weight for height, lung function, or vitamin A levels. The best positive predictor for 25-OH vitamin D less than 40 mmol/l was low vitamin E (66.7%), with a negative predictive value of 94.6%. 25-OH vitamin D levels correlated with vitamin E/cholesterol levels (r = 0.41, P < 0.01) and weakly with vitamin E levels (r = 0.28, P < 0.08), but not with vitamin A levels. CONCLUSIONS: These results suggest that children aged less than 12 years and older children with normal vitamin E levels are especially unlikely to have low 25-OH vitamin D levels, and this measure can therefore be omitted. In contrast, those children with low vitamin E levels may warrant monitoring.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".