Magnitude of the Potential Screening Gap for Fabry Disease in Manitoba, Canada
Bibliographic record
Abstract
Background: Fabry disease is a rare disorder caused by deficient activity of -galactosidase A (GLA) and often leads to heart, kidney, and nerve damage. Fabry disease can be treated with enzyme replacement therapy, improving quality of life, but it often goes undiagnosed as neonatal screening programs suggest its true prevalence is much higher than what has been reported clinically. Given its low frequency, mass screening for Fabry disease is impractical. However, a targeted screening program of high-risk individuals may uncover previously unknown cases. Our objective was to use population-level administrative health databases to identify patients at high risk of Fabry disease. Methods: We conducted a retrospective cohort study of all residents of Manitoba, Canada between 1998 and 2018. Using databases housed at the Manitoba Centre for Health Policy, we ascertained a cohort of patients without a diagnosis of Fabry disease who had at least one of the following high-risk conditions: idiopathic hypertrophic cardiomyopathy, ischemic stroke <45 years of age, kidney failure or proteinuria of unknown cause, peripheral neuropathy. We excluded patients with known contributing factors to these high-risk conditions, including, where appropriate, diabetes, hypertension, autoimmune diseases. cancer, glomerulonephritis, and polycsysitc kidney disease. Those who remained and did not have evidence of GLA testing were considered to have a 0.5-4.0% probability of having Fabry disease. Results: A total of 145,466 individuals had at least one high-risk condition. Of those, 1,386 remained after applying exclusion criteria. Only 22 of 1,386 (1.6%) had GLA testing, leaving a screening gap of 1,364 individuals of which 932 were still alive and residing in Manitoba as of December 31 2018. We estimated that screening these individuals would yield between 4 and 37 new cases of Fabry disease. Conclusions: Administrative health databases may be a useful tool to identify patients at higher risk of Fabry disease or other rare diseases. Further directions include designing a program to screen these individuals for Fabry disease.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".