Family Communication and Cascade Testing for Fragile X Syndrome
Bibliographic record
Abstract
A total of 679 families who had at least one child with fragile X syndrome (FXS) were recruited from a research registry to participate in a survey examining cascade testing and communication about FXS. Families had a total of 1117 children (804 males, 313 females). Most families (84 %) had tested all of their children. The main reason for not testing, which did not differ by gender or age of the child, was that the child did not show signs of FXS (68 %). Families talked with their children about FXS occasionally (47 %) although 16 % said they do not talk about it. Most families (66 %) had told their children their FXS status, with males and those with the premutation being less likely to be told test results. Of those that did not, 46 % said that they would tell their child when they were old enough to understand, whereas 34 % had either decided they would not tell or were not sure if or when they were going to tell. About a quarter of respondents (28 %) indicated that no extended family members had been tested, with income and communication about FXS being the strongest predictors. Results from this large scale survey provide important data on how families communicate about FXS and reasons testing is or is not sought. This information can be used by genetic counsellors in providing follow-up to families after a FXS diagnosis.
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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 | 0.001 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".