Primary Adrenocortical Insufficiency Case Series: Genetic Etiologies More Common than Expected
Bibliographic record
Abstract
BACKGROUND/AIMS: Primary adrenal insufficiency (AI) is an important cause of morbidity in children. Our objectives were: (1) to describe the clinical presentation of children with new-onset primary AI, and (2) to identify monogenic causes of primary AI in children. METHODS: Chart review and mutation detection in candidate genes were conducted for 11 patients with primary AI. RESULTS: The likely cause of AI was determined in 9 patients. One had a homozygous MC2R mutation associated with familial glucocorticoid deficiency. Two had the same homozygous mutation in the AIRE gene which is associated with type 1 autoimmune polyglandular syndrome. One patient had a heterozygous change in this gene of undetermined significance. Five were homozygous for the previously reported p.R188C STAR mutation causing nonclassic lipoid congenital adrenal hyperplasia, representing the largest cohort of such patients from a single geographic area. In the remaining 2 patients, no clear etiology was identified. CONCLUSIONS: We recommend genetic testing for patients who have negative anti-adrenal antibodies or suggestive family history. Diagnosing a genetic etiology can provide information about prognosis and treatment, and is therefore beneficial for patients. Our high proportion of patients with nonclassic lipoid congenital adrenal hyperplasia likely represents a founder effect.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".