Diagnostic accuracy of rectal mucosa biopsy testing for chronic wasting disease within white-tailed deer ( <i>Odocoileus virginianus</i> ) herds in North America
Bibliographic record
Abstract
An effective live animal diagnostic test is needed to assist in the control of chronic wasting disease (CWD), which has spread through captive and wild herds of white-tailed deer (Odocoileus virginianus) in Canada and the United States. In the present study, the diagnostic accuracy of rectal mucosa biopsy sample testing was determined in white-tailed deer from 4 CWD-infected captive herds. Specifically, the current study compared the immunohistochemical detection of disease-associated prion protein in postmortem rectal mucosa biopsy samples to the CWD status of each deer as determined by immunodiagnostic evaluations of the brainstem at the obex, the medial retropharyngeal lymph node, and the palatine tonsil. The effects of age, sex, genotype, and disease progression were also evaluated. Diagnostic sensitivity on rectal biopsy samples for CWD in white-tailed deer ranged from 63% to 100%; the pooled estimate of sensitivity was 68% with 95% confidence limits (95% CLs) of 49% and 82%. However, diagnostic sensitivity was dependent on genotype at prion protein gene (PRNP) codon 96 and on disease progression as assessed by obex grade. Diagnostic sensitivity was 76% (95% CLs: 49%, 91%) for 96GG deer but only 42% (95% CLs: 13%, 79%) for 96GS deer. Furthermore, diagnostic sensitivity was only 36% for deer in the earliest stage of disease (obex grade 0) but was 100% for deer in the last 2 stages of preclinical disease (obex grades 3 and 4). The overall diagnostic specificity was 99.8%. Selective use of antemortem rectal biopsy sample testing would provide valuable information during disease investigations of CWD-suspect deer herds.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".