Bayesian estimation of sensitivity and specificity of a milk pregnancy-associated glycoprotein ELISA test for pregnancy diagnosis between 23 and 27 days after insemination in Holstein dairy cows
Bibliographic record
Abstract
Pregnancy diagnosis using pregnancy-associated glycoprotein (PAG) ELISA technology in blood or milk samples is validated from 28 d after insemination in dairy cows. The objective of this study was to estimate the sensitivity (Se) and specificity (Sp) of a commercial milk PAG-based ELISA in Holstein dairy cows between 23 and 27 d after insemination. Milk samples (n = 268) from 257 Holstein dairy cows 23 to 27 d after AI were submitted for PAG ELISA testing. Pregnancy status was confirmed by either a second milk PAG ELISA test conducted between 28 and 50 d after insemination (n = 200) or transrectal ultrasonography performed between 28 and 59 d after insemination (n = 68). A Bayesian latent class model was used to compare the paired results from the test at 23 to 27 d after AI test to the reference test. The latent class model typically used for comparing 2 or more imperfect tests was extended to include the possibility of pregnancy loss between the 23 to 27 d test and the reference test. Informative priors for the probability of pregnancy loss, and for the Se and Sp of the PAG and ultrasonography reference tests were obtained from the scientific literature. Estimated median Se and Sp of the PAG ELISA test conducted between 23 and 27 d after AI were 0.98 (95% credible interval 0.93 to 1.0) and 0.98 (0.89 to 1.0), respectively, when using a standardized corrected optical density threshold of 0.15. Although the accuracy of the test under investigation was excellent, more data will be needed to confirm the optimal diagnostic cut point for PAG in milk for early pregnancy diagnosis in this time window. The optimal timing of pregnancy diagnosis will depend on herd-specific logistics and the action to be taken to re-inseminate nonpregnant cows.
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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 | 0.018 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".