Distal teat amputation for distal papillary canal injuries in lactating dairy cows
Bibliographic record
Abstract
OBJECTIVE: The objectives were to describe a distal teat amputation and determine the long-term outcomes in dairy cows. METHODS: This retrospective study examined medical records of 22 lactating dairy cows admitted to the Farm Animal Hospital from 2015 to 2021 for distal teat and papillary canal injuries that received a distal teat amputation as the sole surgical treatment. Long-term follow-up was obtained from the Canadian Dairy Network (CDN) and milk producers to determine whether the cows remained in the herd and identify whether any factors affected this outcome. RESULTS: The hindquarters were affected in 18 of the 22 distally amputated teats (82%; 95% CI, 66% to 98%). Mastitis was the most common postoperative complication. Of the 22 cows, CDN and follow-up information was available for 18 cows (82%). Of these 18 cows, 13 (72%; 95% CI, 51% to 93%) remained in the herd for at least the start of a subsequent lactation following surgery. CONCLUSIONS: Lactating dairy cows had a favorable chance of remaining in the herd following a distal teat amputation. CLINICAL RELEVANCE: Decreased milking efficiency caused by distal teat injuries can be a source of economic loss for the milk producer. Distal teat amputation to reestablish milk flow was a practical procedure that required attentive postoperative care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".