Clinical and genetic characterization of a Taiwanese cohort with spastic paraparesis combined with cerebellar involvement
Bibliographic record
Abstract
Hereditary spastic paraplegias (HSPs) are a heterogeneous group of neurodegenerative disorders clinically characterized by progressive lower-limb spasticity. Cerebellar ataxia commonly co-occurs with complicated HSPs. HSP with concurrent cerebellar ataxia has significant clinical and genetic overlaps with hereditary cerebellar ataxia (HCA) and other inherited neurological diseases, adding to the challenge of planning genetic testing for the disease. In this study, we characterized clinical features of a cohort of 24 patients (male/female: 15/9) from 22 families who presented spastic paraparesis combined with cerebellar involvement, with a median disease onset age 20.5 (range 5-53) years. Aside from the core phenotype, 18 (75%) patients had additional neuropsychiatric and systemic manifestations. A stepwise genetic testing strategy stratified by mode of inheritance, distinct neuroimaging features (e.g., thin corpus callosum), population-specific prevalence and whole-exome sequencing was utilized to investigate the genetic etiology. Causative mutations in up to 10 genes traditionally related to HSP, HCA and other neurogenetic diseases (autosomal recessive spastic ataxia of Charlevoix-Saguenay, neurodegeneration with brain iron accumulation, and progressive encephalopathy with brain atrophy and thin corpus callosum) were detected in 16 (73%) of the 22 pedigrees. Our study revealed the genetic complexity of HSP combined with cerebellar involvement. In contrast to the marked genetic diversity, the functions of the causative genes are restricted to a limited number of physiological themes. The functional overlap might reflect common underlying pathogenic mechanisms, to which the corticospinal tract and cerebellar neuron circuits may be especially vulnerable.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".