Canadian Creutzfeldt-Jakob disease incidence remained stable during the coronavirus disease (COVID-19) pandemic
Bibliographic record
Abstract
Introduction: Healthcare disruptions imposed by the coronavirus disease (COVID-19) pandemic and possible biological links between SARS-CoV-2 and prion misfolding might influence the prevalence or characteristics of Creutzfeldt-Jakob Disease (CJD). This report investigates the potential impact of the COVID-19 pandemic on Canadian CJD diagnostics and surveillance from 2016-2025. Methods: Canada-wide CJD diagnostic findings from end-point quaking induced conversion (EP-QuIC) cerebrospinal fluid (CSF) assays were compared across three periods: pre- (2016-01-29 - 2020-02-28), during (2020-03-01 - 2022-09-30), and post-COVID-19 (2022-10-01 - 2025-09-29). Presented are incidence rates and distributions of biomarker abundances, case demographics, CJD molecular subtypes, and disease durations. Results: While EP-QuIC test submissions increased during the pandemic, CJD incidence was unaltered and not associated with SARS-CoV-2 incidence. Demographics, disease durations, and molecular subtypes of sporadic CJD (sCJD) were largely consistent across periods, although a higher proportion of females were tested during COVID and the prevalence of sCJD MV1 declined post-COVID. Conclusion: CJD prevalence and characteristics remained stable during COVID-19 despite increased EP-QuIC test submissions. These findings verify that CJD surveillance in Canada remained vigilant during the pandemic and highlight the value of EP-QuIC CSF testing for comprehensive CJD monitoring.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".