Clinical Stringency Greatly Improves Mutation Detection in Rett Syndrome
Bibliographic record
Abstract
BACKGROUND: Rett syndrome (RTT) is a severe neurodevelopmental disorder of girls, caused by mutations in the X-linked MECP2 gene. Worldwide recognition of the RTT clinical phenotype in the early 1980's allowed many cases to be diagnosed, and established RTT as one of the most common mental retardation syndromes in females. The years since then led to a refinement of the phenotype and the recent elaboration of Revised Diagnostic Criteria (RDC). Here, we study the impact of the presence versus the absence of the use of diagnostic criteria from the RDC to make a diagnosis of RTT on MECP2 mutation detection in Canadian patients diagnosed and suspected of having RTT. METHODS: Using dHPLC followed by sequencing in all exons of the MECP2 gene, we compared mutation detection in a historic cohort of 35 patients diagnosed with RTT without the use of specific diagnostic criteria to a separate more recent group of 101 patients included on the basis of strict fulfillment of the RDC. RESULTS: The MECP2 mutation detection rate was much higher in subjects diagnosed using a strict adherence to the RDC (20% vs. 72%). CONCLUSIONS: These results suggest that clinical diagnostic procedures significantly influence the rate of mutation detection in RTT, and more generally emphasize the importance of diagnostic tools in the assessment of neurobehavioral syndromes.
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 | 0.003 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".