P.129 Worster-Drought syndrome caused by LINS mutations
Bibliographic record
Abstract
Background: Worster-Drought syndrome (WDS) is a congenital, pseudobulbar paresis. Patients show oromotor apraxia causing impaired speech, drooling, dysphagia and varying degrees of cognitive impairment. Familial cases are reported although causative genes have not been identified. LINS mutations have recently been reported in patients with severe cognitive and language impairment. Methods: The proband was diagnosed with WDS at 8 years old because of longstanding drooling, dysphagia and impaired tongue movement. At 14 years old, he remains aphonic, using sign language and typing on a smart-tablet to communicate. Neurological examination including facial and extraocular movement was otherwise unremarkable. MRI brain revealed no heterotopia or atrophy. Results: An expanded intellectual disability panel at GeneDx identified nonsense mutations in LINS alleles: c.1096; p.Glu366X and c.1178 T>G, p.Lys393X. Neuropsychological testing at 14 years old noted nonverbal reasoning skills at 5 year old level with relative sparing of his receptive vocabulary and visual attention. Compared to prior testing at 9 years his receptive language improved from a 6 year old to an 8.5 year old level. Conclusions: Nonsense mutations of LINS have been identified in a patient with WDS. Despite his severe and persistent aphonia, improvements in receptive language were observed with global intellectual functioning better than expected.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".