Clinical, Laboratory, and Imaging Features Associated with Arginine Vasopressin Deficiency (Central Diabetes Insipidus) in Erdheim–Chester Disease (ECD)
Bibliographic record
Abstract
Purpose: Erdheim–Chester disease (ECD) is an L Group Langerhans histiocytosis associated with pathogenic variants within the MAPK pathways, most commonly the BRAF gene. We analyzed prevalence, genetic, biochemical, and pituitary imaging features associated with arginine vasopressin deficiency (AVP-D), one of the most common endocrinopathies in ECD. Methods: A cross-sectional descriptive study of 61 subjects with ECD was conducted at a clinical research center from January 2011 to December 2018, with molecular genetics, baseline biochemical and pituitary endocrine function studies, and dedicated pituitary MRI (or CT) studies. AVP-D and anterior pituitary endocrinopathies (hypothyroidism, hypogonadism, adrenal insufficiency and panhypopituitarism) were assessed. Students’ t-tests, nonparametric tests, Fisher’s exact tests, and logistic regression were employed for analysis. Results: In total, 22 out of 61 subjects (36%; 19 males and 3 females) had AVP-D; 18 subjects with AVP-D were in active treatment with desmopressin. Those with versus without AVP-D were younger [mean (±SD): 50.00 (±10.45) vs. 56.72 (±10.45) years], had higher prevalence of BRAF V600E pathogenic variants [68% vs. 43%], lower IGF-1 [mean (±SD): 137.05 (±67.97) vs. 175.92 (±61.89) ng/mL], lower urine osmolality [416.00 (250.00–690.00) vs. 644.50 (538.75–757.75)) mOsm/kg], and a higher burden of central hypogonadism [81.82% vs. 36.00%], central hypothyroidism [23% vs. 2.5%], panhypopituitarism [41% vs. 0%], anterior pituitary endocrine deficits, absent posterior pituitary bright spots [63.64% vs. 20.51%], and abnormal pituitary imaging. In adjusted models, [OR (95%CI)] BRAF V600E mutation [7.38 (1.84–39.01)], central hypogonadism [6.193 (1.44–34.80)], primary hypothyroidism [13.89 (1.401–406.5)], absent posterior pituitary bright spot [12.84 (3.275–65.04)], and abnormal pituitary imaging [10.60 (2.844–48.29)] were associated with higher odds of having AVP-D. Conclusions: AVP-D is common in ECD and accompanied by a higher burden of pituitary endocrinopathies, BRAF V600E pathogenic variants, abnormal pituitary imaging, and absent posterior pituitary bright spots.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".