Characteristic Analysis and Literature Review of Hereditary Spinocerebellar Ataxia With Lumbar Spondylolisthesis and Valvular Prolapse
Bibliographic record
Abstract
Spinocerebellar ataxia (SCA) is an autosomal dominant disease with high genetic heterogeneity, which cannot be cured until now. According to clinical manifestations or genetic pathology classification, SCA types 1 to 47 have been characterized so far, and the pathogenic genes of 28 SCA types have been identified. The clinical manifestations of the disease are diverse and easy to be misdiagnosed. This study aims to describe the family characteristics, specific clinical manifestations and genotyping of SCA patients. Magnetic resonance imaging (MRI) was used to check the atrophy of the brain and spinal cord. The lumbar spondylolisthesis was examined by computed tomography (CT). The possible influencing factors were analyzed by questioning each member of the patient’s family, especially the persons with the disease, to draw the genetic genealogy. Relevant literatures were searched to compare differences in genotypes between the patient and similar clinical manifestations. Craniocerebral MRI showed that cerebellar sulcus widened and deepened, vermis atrophy; enlargement of the cistern around the brainstem; cerebral cortex atrophy, furrow, fissure widen. Lumbar CT showed L3 spondylolisthesis slightly to the right. Genetic genealogy showed that the children of the patients still had the disease, and the children of the patients without the disease were all normal, which is consistent with the autosomal dominant genetic law. Compared with the literature, the patient had the same clinical manifestations as Machado’s disease: convex eyes, dysarthria, terminal muscles atrophy, ataxia gait, weakened tendon reflex, and arched foot. The same clinical manifestations of SCA40 included ataxia, wide-based gait, poor range discrimination and rotation movement disorder, but there were also many discrepancies. The patient’s lumbar spondylolisthesis and valvular prolapse were not present in any of the previous types. In conclusion, craniocerebral MRI and gene sequencing can help distinguish and diagnosis the subtypes of SCA; whether this patient is a new subtype with lumbar spondylolisthesis and heart valve prolapse needs further study; this genealogy supports that through eugenics dominant genetic diseases being passed on to the offspring can be avoided. J Neurol Res. 2019;9(4-5):81-88 doi: https://doi.org/10.14740/jnr544
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".