Assessment of a commercial borescope to evaluate the presence of lesions of digital dermatitis in dairy cows
Bibliographic record
Abstract
Digital dermatitis (DD) is a worldwide infectious disease of cattle with high prevalence in dairy herds. It is a painful disease with welfare issues causing economical losses. Identifying the affected animals is crucial to establish early treatment and evaluate the efficacy of a control strategy. The "gold standard" diagnosis of DD is the direct observation of DD lesions in a trimming chute. However, the use of a trimming chute for daily diagnosis of DD in all cows is not possible. To facilitate DD monitoring between trimming sessions, lesions could be identified in the parlor during milking. Therefore, we evaluated the use of a commercial borescope in a rotary milking parlor. Our hypothesis was that a borescope is an adequate alternative to evaluate DD lesions between trimming sessions. Our objective was to assess the sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) of a borescope for the diagnosis of DD in the milking parlor as compared to direct observation in a trimming chute, and to quantify the agreement between both techniques.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".