Surgical Treatment of Peripheral Vascular Malformations: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Surgical treatment of peripheral vascular malformations is widely performed as primary and secondary treatments. Excellent results have been reported; however, it is thought that complications are likely to occur because of damage to adjacent structures. This systematic review aimed to elucidate the indications and outcomes of surgical treatment of vascular malformations. METHODS: PubMed, EMBASE, and the Cochrane Central Register of Controlled Trials were searched for studies reporting outcomes of surgery in at least 15 patients with a single type of peripheral soft-tissue vascular malformation. The authors extracted data on patient and lesion characteristics, treatment characteristics, and outcomes (including complications). Meta-analysis was conducted on recurrence and complication rates. RESULTS: A total of 3042 articles were found, of which 24 were included: nine studies on arteriovenous malformations, seven on venous malformations, and eight on lymphatic malformations, totaling 980 patients. Meta-analyses showed pooled proportions for recurrences of 11 percent in arteriovenous malformations, 5 percent in venous malformations, and 9 percent in lymphatic malformations. Pooled proportions of major complications were 9 percent for arteriovenous malformations, 3 percent for venous malformations, and 1 percent for lymphatic malformations. The authors found a 5 percent pooled recurrence proportion in total resections, compared with 28 percent in subtotal resections. The pooled odds ratio for recurrence in total and subtotal resections showed a significant lower recurrence rate after total resection (odds ratio, 0.14, p = 0.02). CONCLUSIONS: Surgical treatment of vascular malformations appears to be effective and safe in many cases. However, it seems that surgery is performed predominantly in small lesions, and subtotal resection has a higher risk of recurrence than total resection.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.012 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".