Optimization of a milk pregnancy-associated glycoprotein enzyme-linked immunosorbent assay test for pregnancy in Holstein cows using time-dependent cut-points between 23 and 90 days after insemination
Bibliographic record
Abstract
Our hypothesis was that the accuracy of a commercial milk pregnancy-associated glycoprotein (PAG) ELISA test using the same thresholds at all stages of gestation would be improved by adjusting the cut-points according to the number of days since last breeding (DSLB) between 23 and 90 d after insemination in Holstein dairy cows. Our objectives were to develop a DSLB-specific set of thresholds that would provide better test performance under field conditions and provide more information for the inconclusive test results, by dichotomizing these into "probably open" and "probably pregnant" categories. Milk samples (n = 182,738) submitted to the Lactanet (Canadian Dairy Herd Improvement) laboratory from 2013 to 2021 for pregnancy testing using a commercial PAG ELISA test were compared with records on insemination outcomes. The data were separated randomly into a training dataset used to develop the DSLB-specific interpretation grid and a validation dataset to quantify its test characteristics and compare the performance of the DSLB-specific and fixed thresholds. Our aim was to achieve negative predictive value >0.99 at all stages of test use and positive predictive value (PPV) ≥0.95 at ≤59 d and ≥0.99 between 60 and 90 d after insemination. Neither approach met these targets between 23 and 25 DSLB. The DSLB-specific interpretation grid had greater PPV than the fixed threshold between 26 and 49 DSLB and met the targets. Both approaches were very near the targets of performance between 50 and 90 DSLB. The DSLB-specific interpretation grid has a similar prevalence of inconclusive test results compared with the fixed threshold but provided additional information on the likelihood of the cow being pregnant or open. Classification of milk PAG results using DSLB-specific cut-points improved the predictive value of pregnancy diagnosis between 23 and 49 d after insemination.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".