An integrated modelling methodology for estimating global incidence and prevalence of hereditary spastic paraplegia subtypes SPG4, SPG7, SPG11, and SPG15
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Hereditary spastic paraplegias (HSPs) are progressively debilitating neurodegenerative disorders that follow heterogenous patterns of Mendelian inheritance. Available epidemiological evidence provides limited incidence and prevalence data, especially at the genetic subtype level, preventing a realistic estimation of the true social burden of the disease. The objectives of this study were to (1) review the literature on epidemiology of HSPs; and (2) develop an epidemiological model of the prevalence of HSP, focusing on four common HSP genetic subtypes at the country and region-level. METHODS: A model was constructed estimating the incidence at birth, survival, and prevalence of four genetic subtypes of HSP based on the most appropriate published literature. The key model parameters were assessed by HSP clinical experts, who provided feedback on the validity of assumptions. A model was then finalized and validated through comparison of outputs against available evidence. The global, regional, and national prevalence and patient pool were calculated per geographic region and per genetic subtype. RESULTS: The HSP global prevalence was estimated to be 3.6 per 100,000 for all HSP forms, whilst the estimated global prevalence per genetic subtype was 0.90 (SPG4), 0.22 (SPG7), 0.34 (SPG11), and 0.13 (SPG15), respectively. This equates to an estimated 3365 (SPG4) and 872 (SPG11) symptomatic patients, respectively, in the USA. CONCLUSIONS: This is the first epidemiological model of HSP prevalence at the genetic subtype-level reported at multiple geographic levels. This study offers additional data to better capture the burden of illness due to mutations in common genes causing HSP, that can inform public health policy and healthcare service planning, especially in regions with higher estimated prevalence of HSP.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it