Estimated prevalence of Niemann–Pick type C disease in Quebec
Bibliographic record
Abstract
Niemann-Pick type C (NP-C) disease is an autosomal recessive disease caused by variants in the NPC1 or NPC2 genes. It has a large range of symptoms depending on age of onset, thus making it difficult to diagnose. In adults, symptoms appear mainly in the form of psychiatric problems. The prevalence varies from 0.35 to 2.2 per 100,000 births depending on the country. The aim of this study is to calculate the estimated prevalence of NP-C in Quebec to determine if it is underdiagnosed in this population. The CARTaGENE database is a unique database that regroups individuals between 40 and 69 years old from metropolitan regions of Quebec. RNA-sequencing data was available for 911 individuals and exome sequencing for 198 individuals. We used a bioinformatic pipeline on those individuals to extract the variants in the NPC1/2 genes. The prevalence in Quebec was estimated assuming Hardy-Weinberg Equilibrium. Two pathogenic variants were used. The variant p.Pro543Leu was found in three heterozygous individuals that share a common haplotype, which suggests a founder French-Canadian pathogenic variant. The variant p.Ile1061Thr was found in two heterozygous individuals. Both variants have previously been reported and are usually associated with infantile onset. The estimated prevalence calculated using those two variants is 0.61:100,000 births. This study represents the first estimate of NP-C in Quebec. The estimated prevalence for NP-C is likely underestimated due to misdiagnosis or missed cases. It is therefore important to diagnose all NP-C patients to initiate early treatment.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".