Teat Dilators as Free Foreign Bodies in the Bovine Teat
Bibliographic record
Abstract
The objective of this study is to report two cases of foreign bodies in teats of cows with milk flow disorders. Foreign bodies and the causes of the milk flow disorders were diagnosed and treated by using teat endoscopy. In the first case, a teat dilator was found in the teat along with inflammation of the teat cistern lining. The milk flow disorder was caused by teat canal skin which had ruptured and inverted into the teat cistern. In the second case, a wax teat insert was found in the teat cistern. The milk flow disorder was caused by a narrowed inner opening of the teat canal. In both cases the milk from the affected quarters showed signs of subclinical mastitis. The foreign bodies were removed through the teat canal by using forceps. The causes of the milk flow disorders were treated surgically. Antibiotics were administered into the affected teats and a sterile silicone implant was inserted into the teat canal. The teat was bandaged and rested for several days. On re-examination four weeks later, milk flow and milk quality were significantly improved. Our findings indicate that the alterations in the teat canal area were the cause of the milk flow disorders rather than the foreign bodies. We conclude that in teats with milk flow disorders, a diagnosis should be made first and then a causal treatment initiated. Teat dilators and wax inserts without heads may slip into the teat and act as foreign bodies. Teat dilators may be deleterious to udder health.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".