Diagnosing Zygosity in Infant Twins: Physical Similarity, Genotyping, and Chorionicity
Bibliographic record
Abstract
We compared the results of different methods for diagnosing zygosity in a sample of 237 same-sex pairs of twins assessed at 5 and 18 months of age. Despite the twins' very young age and early stage of development, physical similarity was concordant with genotyping in 91.9% of cases at 5 months and 93.8% of cases at 18 months, for a subsample of 123 and 113 pairs, respectively. This concordance rate was obtained following a case-by-case assessment of each pair's physical similarity using a shortened version of the Zygosity Questionnaire for Young Twins (Goldsmith, 1991). Taking into account the chorionicity data available from the twins' medical files, we were able to classify correctly 96% of the pairs, an accuracy rate comparable to previously reported rates obtained with older twins. Chorionicity data is especially useful since we found that monochorionic MZ twins are more difficult than dichorionic MZ twins to diagnose by physical similarity at these young ages. The relative cost-benefit of methods based on reported physical similarity and DNA analysis is discussed in light of these results.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".