Preoperative Angiography for Free Fibula Flap Harvest: A Case Series
Bibliographic record
Abstract
Purpose: To assess if preoperative angiography of the lower extremity is necessary to detect abnormalities that alter operative planning of a free fibula flap (FFF). The secondary objective is to determine whether abnormalities are identified on physical examination. Methods: A retrospective case series of patients receiving preoperative lower extremity angiography for FFF was performed. Between November 2004 and July 2016, patients assessed for FFF reconstruction by a single surgeon were reviewed. Outcomes analyzed were preoperative physical examination, angiography findings, changes in operative plan, and perioperative complications including flap failure and limb ischemia. Level of agreement between physical examination and angiography findings was analyzed. Results: A total of 132 consecutive patients were assessed for FFF, of which 70 met the inclusion criteria. Mean age was 60.9 (range: 22-88) years old. All patients underwent aortic angiogram runoff, except for 2 who received computed tomography angiography. The surgical plan was altered based on angiography findings in 9 (12.9%) patients, and 7 (77.8%) of these cases had a normal physical examination. A further 6 (8.6%) patients had physical examination findings precluding the use of FFF, whereas imaging demonstrated the contrary. Physical examination demonstrated low predictability of aberrant vascular anatomy, with a sensitivity of 22.2%. There were no limb ischemia complications. Conclusions: Routine preoperative angiography of the lower extremity for all patients being evaluated for FFF is important to ensure safety and success of the procedure. Physical examination alone is insufficient to detect vascular abnormalities that may result in limb or flap compromise.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".