How often should we screen children with Down's syndrome for hypothyroidism?
Bibliographic record
Abstract
Commentary on the paper by Gibson et al (see page 574) Aside from mental retardation, the prevalence of several health problems is higher in individuals with Down’s syndrome than in the general population. Among these problems, hypothyroidism is often noted.1 At the outset, it is important to distinguish congenital from acquired hypothyroidism. Numerous references continue to state that the prevalence of congenital hypothyroidism is considerably higher in newborns with Down’s syndrome than in the general population.2 However, the evidence for this is unconvincing: in Quebec, there was no case of Down’s syndrome in several hundred children with permanent primary congenital hypothyroidism diagnosed through neonatal screening, suggesting that thyroid dysgenesis (ectopy or agenesis, which account for 85% of cases of congenital hypothyroidism) is not more frequent in Down’s syndrome.3 The coexistence of Down’s syndrome with severe persistent primary congenital hypothyroidism with a normally located gland of normal or increased size (“dyshormonogenesis”) has been reported in a few cases but may represent a chance association.4 On the other hand, the distribution of thyrotrophin (TSH) and thyroxine (T4) measured on neonatal blood spots are slightly shifted to the right and left, respectively, in newborns with Down’s syndrome compared to the general newborn population.5 …
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".