Intensity of human prion disease surveillance predicts observed disease incidence
Bibliographic record
Abstract
BACKGROUND: Prospective national screening and surveillance programmes serve a range of public health functions. Objectively determining their adequacy and impact on disease may be problematic for rare disorders. We undertook to assess whether objective measures of disease surveillance intensity could be developed for the rare disorder sporadic Creutzfeldt-Jakob disease (CJD) and whether such measures correlate with disease incidence. METHOD: From 10 countries with national human prion disease surveillance centres, the annual number of suspected prion disease cases notified to each national unit (n=17,610), referrals for cerebrospinal fluid (CSF) 14-3-3 protein diagnostic testing (n=28,780) and the number of suspect cases undergoing diagnostic neuropathological examination (n=4885) from 1993 to 2006 were collected. Age and survey year adjusted incidence rate ratios with 95% CIs were estimated using Poisson regression models to assess risk factors for sporadic, non-sporadic and all prion disease cases. RESULTS: Age and survey year adjusted analysis showed all three surveillance intensity measures (suspected human prion disease notifications, 14-3-3 protein diagnostic test referrals and neuropathological examinations of suspect cases) significantly predicted the incidence of sporadic CJD, non-sporadic CJD and all prion disease. CONCLUSIONS: Routine national surveillance methods adjusted as population rates allow objective determination of surveillance intensity, which correlates positively with reported incidence for human prion disease, especially sporadic CJD, largely independent of national context. The predictive relationship between surveillance intensity and disease incidence should facilitate more rapid delineation of aberrations in disease occurrence and assessment of the adequacy of disease monitoring by national registries.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".