Investigating the potential role of abrasion in the development of toe tip necrosis in beef cattle: an ex vivo study
Bibliographic record
Abstract
Objective: To compare regional stiffness of the white line (objective 1) and image-based metrics of damage (objective 2) of control claws and claws subjected to an abrasion simulator mimicking animals abrading their claws against a concrete surface commonly found in feedlots. Methods: Sixteen (n = 16) cadaveric bovine hind limbs were acquired from participating commercial feedlots and separated into different testing groups: lateral claws subjected to an abrasion simulation (n = 8) and control claws manually rasped to the same level of wear found after the abrasion simulation (n = 8). Claws were subjected to indentation testing along the white line to determine regional stiffness (control = 8; abraded = 8) and contrast-enhanced, high-resolution imaging (control = 6; abraded = 6) where mean image intensity was used to characterize damage. Analysis of variance was used to compare regional stiffness and image intensity of the different groups. Results: Lower stiffness of the white line along the apical region was noted in abraded claws versus control claws (P < .019). Higher mean intensity (a measure of damage) was found in abraded claws versus control claws (P < .026). Conclusions: Study findings indicate that abraded claws exhibited lower stiffness along the apical region of the white line relative to control claws. Also, analyses of contrast-enhanced, high-resolution imaging data suggested that pathways for foreign material to enter the claw may be present following abrasion. Clinical Relevance: These findings support the premise that abrasion may be involved in white line separation and toe tip necrosis pathogenesis. Alternative floorings that minimize abrasion may be beneficial for avoiding toe tip necrosis.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".