Discordance of Monozygotic Twins for Thyroid Dysgenesis: Implications for Screening and for Molecular Pathophysiology
Bibliographic record
Abstract
Since the advent of biochemical screening for congenital hypothyroidism, the majority of monozygotic twins reported with thyroid dysgenesis have been discordant, and most were missed on neonatal screening, presumably due to fetal blood mixing. We hypothesized that there may be bias leading to preferential reporting of discordant twins and/or of false negative screening results. Therefore, we performed a systematic search for twins in two congenital hypothyroidism screening centers, Quebec and Brussels, that use a primary TSH approach. In Quebec, 10 pairs of twins were identified, all discordant for congenital hypothyroidism due to thyroid dysgenesis (4 monozygotic and 4 dizygotic pairs) and dyshormonogenesis (2 dizygotic pairs). The 6 pairs identified in the Brussels database were also all discordant for congenital hypothyroidism due to thyroid dysgenesis (1 monozygotic and 3 dizygotic pairs) and dyshormonogenesis (2 dizygotic pairs). The median increase in TSH between screening and diagnosis was 7-fold in the monozygotic twins vs. 2-fold in matched singletons (P = 0.02), suggesting fetal blood mixing between the twins. In summary, discordance for thyroid dysgenesis is the rule in monozygotic twins, and fetal blood mixing may result in delayed or missed diagnoses. We therefore conclude that 1) a second sample for congenital hypothyroidism screening at 14 d of age should be considered for all same-sex twins; and 2) thyroid dysgenesis generally results from epigenetic phenomena, early somatic mutations, or postzygotic stochastic events.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".