An Atypical Case of an SCN9A Mutation with Global Motor Delay and Erythromelalgia (IN1-1.010)
Bibliographic record
Abstract
OBJECTIVE: To describe the atypical clinical presentation of a patient with an SCN9A mutation. BACKGROUND: Erythromelalgia is characterized by recurrent episodes of burning pain and redness of the extremities and is caused by heterozygote gain-of-function mutations in the SCN9A gene, coding for the NAv1.7 channel. It usually presents as a pure sensory-autonomic disorder. We describe a patient with an SCN9A mutation and an usual phenotypic presentation of gross motor delay, childhood-onset erythromelalgia, extreme visceral pain episodes, followed by hypoesthesia and automutilation. DESIGN/METHODS: The investigation of the patient9s motor delay included various biochemical analyses, an EMG, a muscle biopsy, and a quantitative PCR of SMN1 . Once erythromelalgia was suspected, the SCN9A gene was sequenced. Sequential therapeutic trials of amitriptyline, gabapentin, carbamazepine and mexiletine were attempted. RESULTS: The EMG, CGH, EEG and metabolic tests were negative. The sural nerve biopsy showed an axonal neuropathy, whereas the muscle biopsy showed signs of neurogenic atrophy. Sequencing of the SCN9A gene revealed a heterozygote missense mutation in exon 7; p.I234T. CONCLUSIONS: We present the first case of global motor delay and erythromelalgia associated with an SCN9A mutation. The gross motor delay might be attributed to the extreme pain episodes or to a developmental perturbation of sensory-motor integration. Disclosure: Dr. Meijer has nothing to disclose. Dr. Vanasse has received personal compensation for activities with Janssen as a speaker. Dr. Vanasse has received research support from L9Institut de recherche Yves Ponroy. Dr. Nizard has nothing to disclose. Dr. Robitaille has nothing to disclose. Dr. Rossignol has nothing to disclose.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".