1271 Economic evaluation of a milk test for pregnancy confirmation in dairy cows
Bibliographic record
Abstract
Timely diagnosis of pregnancy and pregnancy loss is economically important. A commercially available pregnancy-associated glycoprotein milk assay is offered through routine Dairy Herd Improvement (DHI) testing for diagnosis of pregnancy. The objective was to complete a cost-benefit analysis of the milk pregnancy test for confirmation of pregnancy. The test can be used to complement, or as an alternative to, veterinary diagnosis by palpation or ultrasound. CanWest DHI currently recommends using the test for confirmation of pregnancy ≥ 60 d in gestation. Therefore, for this analysis it was assumed cows had been previously diagnosed pregnant. The model included 4 simulated pregnancy confirmation strategies: 1) no confirmatory testing, 2) confirmation by milk PAG test, 3) confirmatory examination by a veterinarian, and 4) confirmation using a combination of the milk test and veterinary exam. The analysis was done by simulations of economic outcomes using a cow-level stochastic model (with @Risk for Excel) with uniform distributions for additional days open due to testing frequency. Model assumptions were that cows became eligible for testing at 60 d in gestation, the herd had biweekly veterinary visits, and was enrolled in DHI milk recording with a milk test every 5 wk. Data from the current literature were used to model input variables associated with losses due to days open (for cows eligible to be re-inseminated after pregnancy loss) and culling after pregnancy loss for cows too late in lactation to re-inseminate. The base cost of veterinary exam was $2 and the milk test was $6. For each scenario, 1,000 simulations were run generating a minimum, maximum, and mean value. The most costly option was no confirmatory testing. The benefit of confirmatory testing compared to no confirmatory testing was between $11.80 and $17.90 per cow. On average, the milk test was $6.10 more costly per cow tested than veterinary confirmation. Under the assumed inputs, milk testing would have to cost < $1.00 or occur weekly to have a lower cost than veterinary confirmation. Sensitivity analysis indicated that the models were most sensitive to the proportion of cows found open and the proportion of open cows eligible to re-inseminate. Models were found to be less sensitive to the cost of a day open, additional days open due to testing frequency, and the cost of the test. Pregnancy loss is a costly event but the cost can be limited by pregnancy confirmation testing.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".