The Familial Aggregation of the Lesser Variant in Biological and Nonbiological Relatives of PDD Probands: a Family History Study
Bibliographic record
Abstract
OBJECTIVE: To determine the risk of the lesser variant (or PDD-like traits) in the biological and nonbiological second- and third-degree relatives of PDD probands using a screening questionnaire and to investigate the extent to which the risk of the lesser variant differs according to various characteristics of the proband. METHOD: The sample consists of a series of 34 nuclear families with 2 affected PDD children (multiplex, MPX), 44 families with a single PDD child (simplex, SPX), and 14 families who adopted a PDD child. Data on characteristics of the lesser variant in 1362 biological and 337 nonbiological second- and third-degree relatives were collected from parents by telephone interview and from several maternal and paternal relatives by questionnaire. RESULTS: All components of the lesser variant were more common in biological relatives (BR) than nonbiological relatives (NBR), confirming the familial aggregation of the traits. Proband characteristics associated with an increased risk of the lesser variant in relatives were a higher level of functioning and coming from a MPX family. CONCLUSIONS: These findings on the familial aggregation of the lesser variant suggest that the genes for PDD also confer susceptibility to the lesser variant and that PDD may be a genetically heterogeneous disorder.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".