The Relationship Between Vitamin D Status and Adrenal Insufficiency in Critically Ill Children
Bibliographic record
Abstract
CONTEXT: Recent studies in critically ill populations have suggested both adrenal insufficiency (AI) and vitamin D deficiency to be associated with worse clinical outcome. There are multiple mechanisms through which these pleiotropic hormones might synergistically influence critical illness. OBJECTIVE: The aim of the study was to investigate potential relationships between vitamin D status, adrenal status, and cardiovascular dysfunction in critically ill children. DESIGN: We conducted a secondary analysis of data from a prospective cohort study. SETTING AND PATIENTS: The study was conducted on 319 children admitted to 6 Canadian tertiary-care pediatric intensive care units. MAIN OUTCOME MEASURES: Vitamin D status was determined through total 25-hydroxyvitamin D (25OHD) levels. AI was defined as a cortisol increment under 9 μg/dL after low-dose cosyntropin. Clinically significant cardiovascular dysfunction was defined as catecholamine requirement during pediatric intensive care unit admission. RESULTS: Using 3 different thresholds to define vitamin D deficiency, no association was found between vitamin D status and AI. Furthermore, linear regression failed to identify a relationship between 25OHD and baseline or post-cosyntropin cortisol. However, the association between AI and cardiovascular dysfunction was influenced by vitamin D status; compared to children with 25OHD above 30 nmol/L, AI in the vitamin D-deficient group was associated with significantly higher odds of catecholamine use (odds ratio, 5.29 vs 1.63; P = .046). CONCLUSIONS: We did not find evidence of a direct association between vitamin D status and critical illness-related AI. However, our results do suggest that vitamin D deficiency exacerbates the effect of AI on cardiovascular stability in critically ill children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".