Bibliographic record
Abstract
Introduction: About 10% of patients with combined pituitary hormones deficiencies (CPHD) have an affected first degree relative, which can be due to mutation of POU1F1 or PROP1 gene. We described 2 siblings with empty sella syndrome. Case Report: A 6 years 3 months old Chinese boy presented with short stature. He had poor growth since 1 year old but remained active and well. He had normal bowel and urinary habits. He has 2 elder sisters, aged 10 years and 8 years. Both parents are non consanguineous and are of normal height. He was not dysmorphic and had primary dentition. His body was proportionate with no skeletal deformity. His weight and height were below 3rd percentile, while head circumference was at 10th percentile. Systemic examination was normal. His growth velocity over a 9-month duration was 2.4 cm/year. He had iron deficiency anemia and was treated with iron supplement. Growth hormone (GH) provocative tests confirmed severe growth hormone deficiency. MRI pituitary in 2007 showed empty sella. GH replacement therapy was initiated at the age of 7 years. Six months later, he had subclinical hypothyroidism. His 8 year-old-sister was also short and later confirmed to have severe GH deficiency. However, she had a normal MRI pituitary. Conclusion: These 2 siblings have familial pituitary hormones deficiencies likely caused by mutation of either PROP1 or POU1F1 gene, which can be confirmed by genetic studies. Department of Paediatrics, Hospital Putrajaya, Putrajaya, Malaysia
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".