Pituitary Dysfunction in Pediatric Patients with Optic Nerve Hypoplasia: A Retrospective Cohort Study (1975–2014)
Bibliographic record
Abstract
BACKGROUND/AIMS: The risk factors for pituitary hormone dysfunction (PHD) in children with optic nerve hypoplasia (ONH) are not well understood. This study identified the type, timing, and predictors of PHD in children with ONH. METHODS: ONH patient charts were reviewed retrospectively. The incidence rate of PHD was calculated assuming a Poisson distribution. Predictors of PHD were identified through a multivariable Cox proportional hazards model. RESULTS: Among 144 subjects with ONH, 49.3% (n = 71) developed PHD over 614.7 person-years of follow-up. The incidence was 11.55 (95% confidence interval [CI]: 9.02-14.57/100 person-years). The median time to first PHD was 2.88 (interquartile range: 0.02-18.72) months. Eighty-two percent developed their first PHD by their 5th and 90% by their 10th birthday, and 89% within 5 years of ONH diagnosis. Prematurity (adjusted hazard ratio [aHR]: 0.33; 95% CI: 0.1-1.07), blindness (aHR: 1.72; 95% CI: 1.03-2.86), maternal substance abuse (aHR: 1.51; 95% CI: 0.91-2.48), abnormal posterior pituitary (aHR: 3.8; 95% CI: 2.01-7.18), and hypoplastic/absent anterior pituitary (aHR: 2.52; 95% CI: 1.29-4.91) were significant predictors of PHD. CONCLUSIONS: The clinical predictors of PHD included blindness, pituitary gland abnormalities, and maternal substance abuse. These predictors help clinical decision-making related to the need for and frequency of hormone testing in pediatric patients with ONH.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".