Clinical and genetic study of hereditary spastic paraplegia in Canada
Bibliographic record
Abstract
Objective: To describe the clinical, genetic, and epidemiologic features of hereditary spastic paraplegia (HSP) in Canada and to determine which clinical, radiologic, and genetic factors determine functional outcomes for patients with HSP. Methods: We conducted a multicenter observational study of patients who met clinical criteria for the diagnosis of HSP in the provinces of Alberta, Ontario, and Quebec from 2012 to 2015. Characteristics of the participants were analyzed using descriptive statistics. The main outcome measure for a subset of the cohort (n = 48) was the Spastic Paraplegia Rating Scale. We also used the SPATAX-EUROSPA disability stage (disability score) to assess disability (n = 65). Results: A total of 526 patients were identified with HSP across the country, and 150 patients had a confirmed genetic diagnosis. Mutations were identified in 15 different genes; the most common were SPAST (SPG4, 48%), ATL1 (SPG3A, 16%), SPG11 (8%), SPG7 (7%), and KIAA0196 (SPG8, 5%). The diagnosis of SPG4 was associated with older age at symptom onset (p = 0.0017). SPG4 and SPG3A were less associated with learning disabilities compared to other subtypes of HSP, and SPG11 was strongly associated with progressive cognitive deficits (odds ratio 87.75, 95% confidence interval 14.04–548.24, p < 0.0001). SPG3A was associated with better functional outcomes compared to other HSP subtypes (p = 0.04) on multivariate analysis. The strongest predictor of significant disability was abnormal brain MRI (p = 0.014). Conclusions: The most important predictors of disability in our HSP cohort were SPG11 mutations and abnormal brain MRI. Accurate molecular characterization of well-phenotyped cohorts and international collaboration are essential to establish the natural history of these rare neurodegenerative disorders.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".