Rett syndrome: A study of the face
Bibliographic record
Abstract
Rett syndrome is a unique disorder of neurodevelopment that is characterized by an evolving behavioral and developmental phenotype, which emerges after an apparently normal early infantile period. It almost exclusively affects females. The face of Rett syndrome is said to resemble that of Angelman syndrome, although there seems little objective support for this impression and it is not a concept with universal support. This observational and anthropometric study was carried out to define the key facial characteristics of females with Rett syndrome and to evaluate whether any changes of significance occur with age. Thirty-seven affected Caucasian females, from 2 to 20 years of age, were evaluated. Thirty-five of them had a documented mutation in MECP2 while the remaining two fulfilled the clinical criteria for Rett syndrome and had been diagnosed by an experienced clinician. Few unusual facial features were noted. Almost all facial measurements were within the normal range although head circumference tended to fall below the normal range with increasing age. The pattern of measurements was constant over time, with the exception of increased facial width in the under 3-year-old girls. The face of Rett syndrome does not demonstrate marked prognathism, wide mouth, spaced teeth or striking microcephaly, all features of Angelman syndrome. Thus, while Rett and Angelman syndromes have similar clinical, neurological, and behavioral phenotypes, they do not appear to share similar facial features.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".