Endoscopic treatment of reflux: management pros and cons
Bibliographic record
Abstract
PURPOSE OF REVIEW: The goal of this review is to contrast the issues in favor of and against the use of endoscopic injection therapy in an attempt to highlight the current state of flux and draw attention to areas that merit further research. RECENT FINDINGS: Current publications have mostly addressed the expanding use of endoscopic injection therapy for vesicoureteral reflux treatment, generally reporting short-term success rates and endpoints. This growing body of literature is presented in the context of perceived benefits vs. disadvantages in comparison with other available treatment modalities. SUMMARY: The management of vesicoureteral reflux has changed dramatically in the past decade, mostly because of the increasing acceptance of endoscopic injection therapy as an adequate, minimally invasive, and effective form of therapy. Recent advances in the composition of injectable materials have allowed for easier placement with a perceived favorable safety profile. In particular, dextranomer/hyaluronic acid has become the injectable material of choice, with quick acceptance and widespread use soon after its introduction in different countries. As we critically evaluate the evolving treatment options, the presented literature helps draw attention to some of the challenges we face and the need for long-term and carefully planned prospective studies to support our interventions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".