A.07 Genomics of atypical dyskinetic cerebral palsy – opportunities for improved diagnosis and management
Bibliographic record
Abstract
Background: Cerebral palsy (CP) is a debilitating disorder (1). Based on neuromotor impairments it is divided to spastic, dyskinetic and ataxic types (2). Inborn Errors of Metabolism (IEMs), monogenic and chromosomal disorders mimic CP (3). We aimed to identify causal genetic variants in patients with atypical dyskinetic CP in whom known IEMs were ruled out. Timely diagnosis is essential for proper management, especially in conditions that mimic CP and are treatable. Methods: We enrolled 23 patients with unexplained atypical dyskinetic CP, for whole exome sequencing. Variants were filtered against public and in-house databases to identify variants predicted as damaging (in silico tools and ACMG criteria). We applied a virtual gene panel of known and suspected CP and movement disorder genes and investigated each sample. Results: The participants presented with symptoms including: spasticity, dystonia, choera-athetosis, ataxia and cognitive delays. We identified 23 diagnoses: 13 dominant,6 recessive and 4 X-linked. 12 patients had movement disorders. In 4, the diagnoses enabled targeted treatment (neurotransmitter supplements in Unverricht Lundborg diseases (CSTB) and PAK3 deficiency, deep brain stimulation in GNAO1 deficiency, medical diet in Glutaric Aciduria (GCDH). Conclusions: Whole Exome Sequencing contributes to establishing diagnosis in patients with atypical dyskinetic CP resulting in precision medicine and improved health outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".