The use of near infrared fluorescence imaging with indocyanine green for vascular visualization in caudal auricular flaps in two cats
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Bibliographic record
Abstract
OBJECTIVES: To describe the use of near infrared fluorescence (NIRF) imaging with indocyanine green (ICG) for vascular visualization in two caudal auricular axial pattern flaps (APF). ANIMALS: Two client-owned cats with upper eyelid masses. STUDY DESIGN: Clinical case report. METHODS: Wide surgical excision with enucleation was performed by using a caudal auricular APF for closure. Flap margins and perforating artery location were approximated with anatomical landmarks. The caudal auricular artery origin was then visualized percutaneously by using an exoscope with NIRF camera and light source after a single 2.5-mg dose of IV ICG. Margins were adjusted as required. The flaps were routinely elevated with continued intraoperative visualization of the artery and rotated to complete closure. RESULTS: After IV ICG administration, fluorescence was initially visualized after 15 to 18 seconds and remained visible for up to 26 minutes. The achieved visualization led to flap margin adjustments in cat 1. Both cats recovered with minimal flap congestion, excellent hair regrowth, and no long-term complications (>186 days). Cat 1 experienced 100% flap survival. Cat 2 experienced 10% partial thickness flap necrosis, but revision was not required, and the flap was healed at recheck 85 days postoperatively. CONCLUSION: The use of ICG for APF vessel visualization prior to and during flap elevation resulted in transcutaneous visualization of the perforating vessel and improved awareness of vessel location intraoperatively. These two cats experienced excellent flap survival without major complications. This report highlights the potential benefits of ICG NIRF in APF for animals undergoing reconstructive surgery.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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