Associations between Pituitary Imaging Abnormalities and Clinical and Biochemical Phenotypes in Children with Congenital Growth Hormone Deficiency: Data from an International Observational Study
Bibliographic record
Abstract
BACKGROUND/AIMS: Magnetic resonance imaging (MRI) is used to investigate the etiology of growth hormone deficiency (GHD). This study examined relationships between MRI findings and clinical/hormonal phenotypes in children with GHD in the observational Genetics and Neuroendocrinology of Short Stature International Study, GeNeSIS. METHODS: Clinical presentation, hormonal status and first-year GH response were compared between patients with pituitary imaging abnormalities (n = 1,071), patients with mutations in genes involved in pituitary development/GH secretion (n = 120) and patients with idiopathic GHD (n = 7,039). RESULTS: Patients with hypothalamic-pituitary abnormalities had more severe phenotypes than patients with idiopathic GHD. Additional hormonal deficiencies were found in 35% of patients with structural abnormalities (thyroid-stimulating hormone > adrenocorticotropic hormone > luteinizing hormone/follicle-stimulating hormone > antidiuretic hormone), most frequently in patients with septo-optic dysplasia (SOD). Patients with the triad [ectopic posterior pituitary (EPP), pituitary aplasia/hypoplasia and stalk defects] had a more severe phenotype and better response to GH treatment than patients with isolated abnormalities. The sex ratio was approximately equal for patients with SOD, but there was a significantly higher proportion of males (approximately 70%) in the EPP, pituitary hypoplasia, stalk defects, and triad categories. CONCLUSION: This large, international database demonstrates the value of classification of GH-deficient patients by the presence and type of hypothalamic-pituitary imaging abnormalities. This information may assist family counseling and patient management.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".