Prevalence of hypocalcaemia and its associated features in 22q11·2 deletion syndrome
Bibliographic record
Abstract
BACKGROUND: 22q11.2 deletion syndrome (22q11.2DS) is a relatively common yet under-recognized genetic syndrome that may present with endocrine features. We aimed to address the factors that contribute to the high prevalence of hypocalcaemia. METHODS: We investigated hypocalcaemia in a well-characterized sample of 138 adults with 22q11.2DS (65 m, 73 F; mean age 34.2, SD 11.8, years) using laboratory studies and lifelong medical records. Logistic regression modelling was used to identify features associated with lifetime prevalence of hypocalcaemia. RESULTS: Of the total sample, 111 (80.4%) had a lifetime history of hypocalcaemia. Eleven (84.6%) of 13 subjects with neonatal hypocalcaemia had documented recurrence of hypocalcaemia. Lifetime history of hypocalcaemia was associated with lifetime prevalence of hypoparathyroidism (P < 0.0001) and hypothyroidism (P = 0.04), as statistically independent factors. Hypomagnesaemia was associated with concurrent hypocalcaemic measurements, especially in the presence of concurrent hypoparathyroidism (P = 0.02). CONCLUSIONS: The results suggest that, in addition to the major effect of hypoparathyroidism, hypothyroidism may play a role in hypocalcaemia in 22q11.2DS and that there is a high recurrence rate of neonatal hypocalcaemia. Hypomagnesaemia may contribute to hypocalcaemia by further suppressing parathyroid hormone (PTH). Although further studies are needed, the findings support regular lifelong follow-up of calcium, magnesium, PTH and TSH levels in patients with 22q11.2DS. At any age, hypocalcaemia with hypoparathyroidism and/or hypothyroidism may suggest a diagnosis of 22q11.2DS.
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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) | 0.000 | 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".