Bayesian estimation of diagnostic accuracy of fecal smears, fecal PCR and serum ELISA for detecting Mycobacterium avium subsp. paratuberculosis infections in four domestic ruminant species in Saudi Arabia
Bibliographic record
Abstract
Paratuberculosis, a chronic wasting disease affecting domestic and wild ruminants worldwide, is caused by Mycobacterium avium subsp. paratuberculosis (MAP). Various diagnostic tests exist for detecting MAP infection; however, none of them possess perfect accuracy to be qualified as a reference standard test, particularly due to their notably low sensitivity. Therefore, we used Bayesian latent class models (BLCMs) to estimate diagnostic accuracy of fecal smears (FS), fecal PCR and serum ELISA for detecting MAP infections in sheep, goats, cattle, and camels older than 2 years in Saudi Arabia. Data from a cross-sectional study conducted in the Eastern Province of Saudi Arabia on 31 different farms with a history of MAP infection were analyzed. Fecal and blood samples from all animals older than 2 years in each farm were collected, resulting in a total of 220 sheep, 123 goats, 66 cattle, and 240 camels sampled. FS and IS900-PCR were performed on fecal samples to detect acid-fast bacilli and MAP DNA, respectively. The IDEXX ELISA kit was used to detect MAP antibodies in serum samples. For each ruminant species population, a BLCM was fitted to obtain posterior estimates [medians and 95 % Bayesian credible intervals (95 % BCI)] for sensitivity (Se) and specificity (Sp) of the three tests. We assumed FS and PCR to be conditionally dependent on the true animal MAP status. Prior distributions for test accuracy were used if available. FS had the highest Se among all tests and across all species with median values around 80 % in sheep, goats and camels, and near 50 % in cattle. Median Sp estimates of ELISA and PCR were higher than 90 % for all species. FS yielded the lowest Sp of the study when applied in camels, sheep, and goats. Using the prevalence observed in this study, median positive predictive value (PPV) was higher for PCR and ELISA than FS for camels, sheep, and goats. In cattle, PPV of all tests was similar with median estimates > 95 %. In camels, sheep, and goats, median negative predicative value (NPV) of all tests were > 60 %. The lowest median NPV for all tests were observed in cattle (< 30 %). Our results suggest that ELISA is a suitable option to identify MAP infected animals in farms with previous history of MAP in the Eastern region of Saudi Arabia.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".