Foam sclerotherapy compared with liquid sclerotherapy for the treatment of lower extremity varicose veins
Bibliographic record
Abstract
BACKGROUND: There is a continued discussion on which is the best sclerosant to treat lower extremity varicose veins. Therefore, we did this meta-analysis to determine that foam sclerotherapy versus liquid sclerotherapy, which could perform better in the treatment of lower extremity varicose veins. MATERIALS AND METHODS: We independently searched 5 databases from inception to February 1, 2019, for randomized controlled trials and prospective controlled trials for comparing foam sclerotherapy and liquid sclerotherapy for the treatment of lower extremity varicose veins. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of studies. The primary outcome and secondary outcomes were analyzed using stata 15.0. This meta-analysis was performed according to Cochrane Handbook. RESULTS: There were significant differences in effective rate (P < .001, odd ratios = 5.64, 95% confidence interval = 3.93-8.10) and incidence rate of pain (P = .030, odd ratios = 1.52, 95% confidence interval = 1.04-2.21) between foam sclerotherapy and liquid sclerotherapy. And there were no significant differences among local inflammation (P = .896, rate difference = 0.00, 95% confidence interval = -0.03 to 0.03), thrombophlebitis (P = .90, rate difference = 0.00, 95% confidence interval = -0.02 to 0.02) and hyperpigmentation (P = .336, rate difference = 0.05, 95% confidence interval = -0.05 to 0.14). CONCLUSIONS: Although foam sclerotherapy has a higher incidence rate of complications, it could achieve a more stable clinical efficacy in the treatment of lower extremity varicose veins than liquid sclerotherapy.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.021 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".