Is the caudal auricular axial pattern flap robust? A multi‐centre cohort study of 16 dogs and 12 cats (2005 to 2016)
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVE: To determine the frequency and type of healing complications arising after the use of the caudal auricular axial pattern flap to close defects on the head in dogs and cats. MATERIAL AND METHODS: Multi-centre retrospective cohort study. Centres were recruited by the Association for Veterinary Soft Tissue Surgery Research Cooperative. Medical records of 11 centres were reviewed, and data from all dogs and cats treated with a caudal auricular axial pattern flap were retrieved. The following data were recorded: signalment, reason for reconstruction, flap dimensions, anatomic landmarks used, histological diagnosis, flap healing and whether revision surgery was required. RESULTS: Twenty-eight cases were included: 16 dogs and 12 cats. Flap length: width ratio was approximately 3:1 and flap length extended to the scapular spine in most cases. Optimal wound healing occurred in five of 16 (31%) dogs and six of 12 (50%) cats. Wound dehiscence without flap necrosis occurred in one of 16 (6%) dogs and one of 12 (8%) cats. Wound dehiscence with flap necrosis occurred in 10 of 16 (63%) dogs and five of 12 (42%) cats. Revision surgery was performed in eight of 16 (50%) dogs and three of 12 (25%) cats. CLINICAL SIGNIFICANCE: The caudal auricular axial pattern flap can provide full thickness skin coverage for large defects on the head in dogs and cats. Partial flap necrosis is a common complication, and revision surgery may be required in order to achieve final wound closure.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it