Preoperative Angiography for Free Fibula Flap Harvest: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The necessity for routine preoperative imaging for free fibula harvest is controversial. The primary objective of this meta-analysis is to determine if lower extremity angiography is necessary to detect abnormalities that may alter flap selection. The secondary objective is to determine if physical examination alone is sufficient to predict these abnormalities. METHODS: A literature search was performed using Cochrane, CENTRAL, MEDLINE, CINAHL, and EMBASE. Studies were selected for inclusion if they included patients undergoing free fibula flap harvest with preoperative imaging, with or without physical examination findings. Data extraction was performed independently and in duplicate, including a change in flap selection and the level of agreement between physical examination and imaging. Pooled proportions were calculated using a random-effects model and 95% confidence intervals (CI). RESULTS: Sixteen studies were included for analysis. Mean sample size was 42 patients (range: 5-123). Included studies were of low methodologic quality. Pooled proportion of patients who had flap selection change secondary to abnormalities identified on preoperative angiography was 20.1% (95% CI: 9.6-33.2%). A pooled proportion of 71.5% (95% CI: 5-88.7%) of cases requiring change in flap selection was missed by physical examination findings alone. CONCLUSION: There is low-quality evidence suggesting a necessity for routine preoperative angiography for all patients undergoing free fibula flap harvest. Physical examination alone is insufficient in detecting vascular abnormalities that may result in limb compromise or an inability to successfully harvest a free fibula. Further investigation is warranted for cost-effectiveness of preoperative imaging protocols.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.046 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".