Bibliographic record
Abstract
Epilepsy is among the most frequent findings in many, especially autosomal, chromosome aberrations. Its incidence, however, is very variable, and there are very few aberrations in which epilepsy is a constant finding. Even siblings and monozygotic twins with the same aberration are often discordant for seizure disorders. Similar observations can be made for congenital (major) malformations in chromosome aberrations. The common explanation is that in these instances epilepsy is not caused by the action of a single gene in single or triple dose, but is influenced by the combined action of a number of genes within and outside of the aneuploid segment. The situation is comparable to a polygenic model of inheritance. Gene mutations associated with epilepsy are known, to date, only for two disorders: the lissencephaly 1 gene in Miller-Dieker syndrome and mutations in the UBE3A gene in Angelman syndrome. Chromosome aberrations in which epilepsy is a major and consistent finding include Angelman syndrome due to loss of the maternal 15q11.2-q12 segment, tetrasomy of the maternal segment 15pter-q13 due to an additional inv dup chromosome, Miller-Dieker syndrome due to deletion of the 17p13.3 segment including the lissencephaly1 gene, ring chromosome 20, and Wolf-Hirschhorn syndrome due to deletion of at least the 4p16.3 segment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".