Why Zygosity of Multiple Births is not Always Obvious: An Examination of Zygosity Testing Requests From Twins or Their Parents
Bibliographic record
Abstract
This paper examines why parents of twins or adult twins themselves request zygosity testing. Of 405 multiples including 8 sets of triplets, the majority (93%) were monozygotic. Age of testing ranged from 0 days to 73 years. About 50% of requests came from parents or twins who were curious about, or expressed a need to be certain of, their zygosity. Other reasons included health concerns (current or future), other twins in the family, and misinformation about zygosity, frequently because of the erroneous assumption that all dichorionic twins are dizygotic. Parents of monozygotic twins may expect their twins to be 'identical' and believe their twins to be dizygotic because of minor phenotypic differences between them. Dizygotic twins like other siblings may share a phenotypic resemblance. Health professionals should be aware that zygosity of multiples may not always be obvious to parents and that accurate knowledge of zygosity may be justified.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".