Diagnosis of adrenal insufficiency in children: a survey among pediatric endocrinologists in North America
Bibliographic record
Abstract
OBJECTIVES: Adrenal insufficiency (AI) is a life-threatening condition where an accurate diagnosis is critical. While the ACTH stimulation test is the diagnostic test of choice, there remains uncertainty around its protocols and interpretation of results. In this context, the objective of this study was to understand practices of North American pediatric endocrinology providers on the diagnosis of AI in children. METHODS: An anonymous electronic survey was sent to members of the Pediatric Endocrine Society. RESULTS: A total of 221 participants were included. The majority practiced in academic centers (78%). All respondents ordered ACTH stimulation tests. While 85% used high-dose ACTH stimulation tests (HDST) to diagnose primary AI, there was less consistency in the choice of tests (HDST vs. low-dose ACTH stimulation test; LDST) when diagnosing secondary AI. When interpreting results, 95% used peak cortisol levels, 70% considered the clinical picture, and 49% used relative increase in cortisol levels. Median (IQR) cortisol cutoff level after ACTH stimulation test that was considered sufficient was 18 (15.5-18) μg/L [496 (428-496) nmol/L]; 17% used different cutoffs for LDST, and 18% used different cutoffs for newborns. Finally, 47% were unaware of the assay that was used in their institution for cortisol measurements. CONCLUSIONS: Pediatric endocrinology providers use ACTH stimulation tests variably, including in the choice between HDST vs. LDST, test protocols, and interpretation of results.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".