Movement disorders in hereditary spastic paraplegia (HSP): a systematic review and individual participant data meta-analysis
Bibliographic record
Abstract
BACKGROUND: Hereditary spastic paraplegia (HSP) is a rare genetic disorder associated with mutations in > 80 loci designated SPG (SPastic parapleGia). The phenotypic spectrum of HSP can extend to include other neurologic features, including movement disorders. Our aim was to investigate genotype-phenotype associations in HSP with a focus on movement disorders. METHODS: We performed a systematic review and individual participant data (IPD)-level meta-analysis by retrieving publications from Medline/EMBASE/Web of Science on HSP with a SPG genotype. Studies were included only if individual-level information was accessible and at least one patient with a movement disorder was reported for that genotype. Out of 21,957 hits, 192 manuscripts with a total of 1413 HSP cases were eligible. Data were compared between two HSP groups: manifested with (HSP-MD, n = 767) or without (HSP-nMD, n = 646) a movement disorder. RESULTS: The HSP-MD group had an older age of onset (20.5 ± 16.0 vs. 17.1 ± 14.2 yr, p < 0.001) and less frequent autosomal dominant inheritance (7.6% vs. 30.1%, p < 0.001) compared to HSP-nMD. SPG7 (31.2%) and SPG11 (23.8%) were the most frequent genotypes in the HSP-MD group. HSP-MD with SPG7 had higher frequency of later onset during adulthood (82.9% vs. 8.5%), ataxia (OR = 12.6), extraocular movement disturbances (OR = 3.4) and seizure (OR = 3.7) compared to HSP-MD with SPG11. Conversely, SPG11 mutations were more frequently associated with consanguinity (OR = 4.1), parkinsonism (OR = 7.8), dystonia (OR = 5.4), peripheral neuropathy (OR = 26.9), and cognitive dysfunction (OR = 34.5). CONCLUSION: This systematic IPD-level meta-analysis provides the largest data on genotype-phenotype associations in HSP-MD. Several clinically relevant phenotypic differences were found between various genotypes, which can possibly facilitate diagnosis in resource-limited settings.
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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 | 0.016 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.027 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".