Persistent hyperinsulinaemic hypoglycaemia in children with Rubinstein–Taybi syndrome
Bibliographic record
Abstract
OBJECTIVE: Genetic aetiology remains unknown in up to 50% of patients with persistent hyperinsulinaemic hypoglycaemia (HH). Several syndromes are associated with HH. We report Rubinstein-Taybi syndrome (RSTS) as one of the possible causes of persistent HH. Early diagnosis and treatment of HH is crucial to prevent hypoglycaemic brain injury. DESIGN: Four RSTS patients with HH were retrospectively analysed. METHODS: Genetic investigations included next-generation sequencing-based gene panels and exome sequencing. Clinical characteristics, metabolic profile during hypoglycaemia and treatment were reviewed. RESULTS: Disease-related EP300 or CREBBP variants were found in all patients, no pathogenic variants were found in a panel of genes associated with non-syndromic HH. Two patients had classic manifestations of RSTS, three had choanal atresia or stenosis. Diagnosis of HH varied from 1 day to 18 months of age. One patient was unresponsive to treatment with diazoxide, octreotide and nifedipine, but responded to sirolimus. All required gastrostomy feeding. CONCLUSIONS: Given the rarity of RSTS (1:125 000) and HH (1:50 000), our observations indicate an association between these two conditions. We therefore recommend that clinicians should be vigilant in screening for HH in symptomatic infants with RSTS. In children with an apparent syndromic form of HH, RSTS should be considered in the differential diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".