Sibship stability of genotype and phenotype in myotonic dystrophy
Bibliographic record
Abstract
Myotonic dystrophy (DM1) is caused by an unstable CTG repeat expansion. Despite the evidence of birth order effect in congenital DM1, the expansion's dynamics among sibships is still unknown. The objective of this study was to determine phenotype and CTG repeat size variability in DM1 sibships, and to investigate their predictive values. We compared 86 sib pairs for CTG repeat, 61 for age at onset and 89 for DM1 phenotype. CTG repeats remained stable for 66 of the 86 sib pairs, including 25 of 27 maternal transmissions and 30 of 42 paternal transmissions. Variations of less than 10 years in the age at onset were observed in 44 of 61 sib pairs, including 16 of 18 maternal transmissions and 19 of 28 paternal transmissions. The same phenotypic severity or a variation of only one class was observed among 86 of the 89 sib pairs, including all of the 35 maternal transmissions and 30 of the 33 paternal transmissions. Birth order, intergenesic interval, oldest sib's CTG repeat or parental age and CTG repeat did not exert any significant influence. These results suggest that genotype and phenotype remained stable among sibs, although the paternal origin of the mutation seemed to reduce the predictability of the severity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".