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 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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".