Bivariate models for co‐aggregation of dichotomous traits in twins
Bibliographic record
Abstract
The study of twins is widely used for research into genetic and environmental influences on human traits. Study designs in which independent samples of monozygotic and dizygotic twins are compared with respect to their similarity have focused on single binary trait. However, the risk of co-occurrence of more than one disease in twins is a parameter of interest to genetic epidemiologists and other investigators. For example, they may be interested in assessing the genetic and environmental etiologies of reading deficits (RD) and attention deficit hyperactivity disorder (ADHD) and their co-morbidity. In this paper, we present statistical methodology for the estimation of twin similarity with respect to two dichotomous traits measured on each member of the twin pair. For two sample problems, several test procedures that account for the correlation between twins and the correlation between traits are presented. An example based on the Australian population-based twin registry published data is given to illustrate the methodology.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".