Sclerotherapy in the Treatment of Hydroceles: A Comprehensive Review of the Efficacy, Types of Sclerosants, and Comparative Outcomes Against Hydrocelectomy
Bibliographic record
Abstract
While hydrocelectomy is the gold-standard for treating hydroceles, it poses an increased risk to patients and a greater burden to the healthcare system. Sclerotherapy is an alternative treatment for hydroceles that involves injecting a sclerosant into the hydrocele under ultrasound guidance. This literature review aimed to assess the types of sclerosants used and how sclerotherapy compares to hydrocelectomy. A literature search was conducted of MEDLINE and EMBASE using the terms "sclerotherapy" and "hydrocelectomy," which yielded 1058 studies, of which 29 met the inclusion criteria. Only studies published after 2000 were included to ensure the most recent information was reviewed. The results showed hydrocele sclerotherapy is done using a variety of sclerosants. The most used agents are polidocanol, phenol, and STS. Of these, phenol had the highest clinical success rate of 96.5%. There was evidence for the use of atypical agents, such as tetracycline antibiotics, which yielded cure rates up to 93%, and alcohol, which was found to be especially useful for treating multiseptated hydroceles. The results comparing sclerotherapy to hydrocelectomy indicated hydrocelectomy to be a more effective method in completely curing hydroceles. However, this came at the cost of more complications. Additionally, sclerotherapy was found to be more advantageous for secondary outcomes, such as healthcare costs and burden to patients. In conclusion, this review shows that while hydrocelectomy is more effective, sclerotherapy is a valuable alternative for treating hydroceles. Due to the lack of standardization among studies, a definitive conclusion cannot be made regarding which sclerosant is best to use.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".