Short communication: Field evaluation of a pregnancy confirmation test using milk samples in dairy cows
Bibliographic record
Abstract
The objective was to validate the performance under field conditions of a novel commercially available ELISA for confirmation of pregnancy using measurement of pregnancy-associated glycoproteins in milk samples from dairy cows. The target population was cows previously diagnosed pregnant by veterinary examination and ≥ 60 d of gestation. On 8 farms milking Holstein cows, milk samples were collected during routine Dairy Herd Improvement testing and shipped overnight to the Dairy Herd Improvement laboratory where the milk pregnancy test was performed. On the same day that milk samples were collected, transrectal palpation was performed by a veterinarian to confirm pregnancy status. Data were available from 683 cows, of which 661 were pregnant and 22 were not pregnant based on veterinary diagnosis, which was taken as the reference test. Based on the manufacturer's interpretive guidelines, 3.8% of test results were classified as "recheck," between the cut-points for classification of pregnant and nonpregnant and were not used in the analysis. The milk pregnancy test performance (and 95% confidence intervals) for confirmation of pregnancy was sensitivity of 99.2% (98.2 to 99.7%) and specificity of 95.5% (78.2 to 99.2%). Given a prevalence of 97% pregnant cows in the sample, the positive predictive value of the milk test was 99.8% (99.1 to 99.96%) and the negative predictive value was 80.8% (61.3 to 90.9%). When used to confirm pregnancy status or detect fetal losses at ≥ 60 d gestation in cows previously diagnosed pregnant, the recommended action for cows with a milk pregnancy-associated glycoprotein test result of not pregnant is veterinary reexamination of the animal to confirm the presence or absence of a viable fetus before reinsemination or administration of prostaglandin.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 teacher head, 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".