Real-time pathologist-assisted field postmortem examinations of beef cattle
Bibliographic record
Abstract
Postmortem examination of deceased production animals with appropriate ancillary testing is fundamental to determining causes of morbidity and mortality. Reaching a definitive diagnosis is crucial to evidence-based herd management and treatment decisions that safeguard animal health and welfare, food safety, and human health. However, for a range of reasons, carcasses sometimes cannot be examined in a veterinary diagnostic laboratory. As a result, postmortem examinations of farmed animals, including cattle, are often performed on-farm by the referring veterinarian (rVet) with tissue samples submitted to a veterinary diagnostic laboratory for ancillary testing. For various reasons, field postmortems can be associated with lower diagnostic rates. We investigated real-time pathologist-assisted field postmortem examination (rtPAP) assistance to beef cattle rVets to gauge any improvement in attaining a final diagnosis. We found that rtPAPs improved the success of reaching a final diagnosis compared to unassisted field postmortem examinations. Both the participating bovine rVets and the pathologists saw benefits to the rtPAPs, with bovine rVets indicating that they would utilize this service in the future if available. Our proof-of-concept study demonstrated the positive role of rtPAPs in diagnosing beef cattle disease and speaks to the need for telepathology services supporting food animal rVets and producers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".