Choosing the right cutoff level of milk progesterone to determine pregnancy status of dairy cows on day 21 post-breeding
Bibliographic record
Abstract
A field study was conducted between February and November 1995 in 3 Holstein dairy herds (193 inseminations) to determine the overall accuracy and usefulness of the skim milk progesterone pregnancy test on day 21 post-breeding, and to examine the effects of using different cutoff values and conception rates on predictive values of this pregnancy test. Results of the milk progesterone test were compared with pregnancy diagnosis after 25 days post-breeding using transrectal ultrasonography and palpation as a standard. The conception rate of the study population based on transrectal palpation was 42%. The relative sensitivity, specificity, and positive and negative predictive values of the milk progesterone test at a cutoff level of 1 ng/ml were 90, 67, 66 and 90%, respectively. It is suggested that in a herd population with a conception rate of 42%, the milk progesterone test is not a good predictor of pregnancy because the proportion of false positive results was high at 19% (37 of 193). The capacity of the test to detect open cows is acceptable with an overall proportion of false negative tests of 4% (8 of 193). Because 8 of 83 cows tested negative were subsequently diagnosed pregnant by transrectal examinations, this milk progesterone test would be expected to result in a 10% probability of misdiagnosing pregnant cows as non-pregnant. If this test was applied in the field, the 1 ng/ml cutoff value would be expected to result in optimum probabilities (>90%) in predicting non-pregnancy in dairy herds with a conception rate of <50%. Herds with a conception rate >50% would not be expected to benefit as much from the test since the predictive value of a negative test is <90%. Finally, considering that a false negative diagnosis is more costly than a false positive, the optimum cutoff value was also calculated at a 3:1 ratio in favour of finding fewer false negatives. In this case, the optimum cutoff value for the herd with a 42% conception rate was 1.2 ng/ml. This study shows that the usefulness of the progesterone test relies on assessing the right cutoff level for the milk progesterone pregnancy test which relies not only on the measurement itself, but also on the expected conception rate and the ratio between false negative and false positive test results.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".