Validity of purulent vaginal discharge, esterase, luminometry, and three bacteriological tests for diagnosing uterine infection in dairy cows using Bayesian latent class analysis
Bibliographic record
Abstract
This prospective cross-sectional study aimed to evaluate the ability of laboratory bacterial culture, Petrifilm, Tri-Plate, luminometry, purulent vaginal discharge (PVD), and esterase to correctly identify uterine infection in dairy cows, and to assess these tests' usefulness in different situations. We sampled dairy cows between 29 and 43 days in milk in seven farms. We considered all six tests imperfect to identify uterine infection and used Bayesian latent class analyses to estimate their sensitivity and specificity. We created ten scenarios, including tests alone, in series, or in parallel, and we calculated predictive values and misclassification cost terms (MCTs). All estimates are presented with 95 % Bayesian credibility intervals (BCI). A total of 326 uterine samples were collected. The laboratory culture had the best validity (sensitivity = 0.87, 95 % BCI = 0.77-0.97; specificity = 0.71, 95 % BCI = 0.58-0.86). The other tests had similar specificity but lower sensitivity, with PVD having the lowest sensitivity (0.05, 95 % BCI = 0.01-0.10). If treating a healthy cow was considered worse than leaving a cow with a uterine infection untreated, luminometry yielded an MCT similar to the laboratory culture. These findings highlight that the on-farm tools currently used to identify cows that could benefit from intrauterine antimicrobial treatment do not identify uterine infection accurately. While the laboratory culture was the most accurate test, it cannot easily be implemented on farms. Luminometry's validity was good, but additional research is necessary to understand how it can be implemented to improve judicious intrauterine antimicrobial use.
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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 | 0.039 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".