Endoscopic injection therapy for treatment of vesicoureteric reflux: A 20-year perspective
Bibliographic record
Abstract
OBJECTIVE: To review the application and outcome of endoscopic injection therapy for vesicoureteric reflux in regard to its evolution over the past two decades. DATA SOURCES: Review articles, original reports and abstracts pertaining to endoscopic injection therapy were obtained through a PubMed search of English, German and French publications from 1981 to 2001. DATA SELECTION: A total of 46 studies were selected. Four were selected to support basic concepts in the management of vesicoureteric reflux, and the remainder pertained specifically to endoscopic injection therapy for vesicoureteric reflux. DATA EXTRACTION: The reports were analyzed with focus on the physical properties of the biomaterial injected, results of treatment in regard to the cure of vesicoureteric reflux, duration of cure, and possible adverse effects and clinical benefits engendered by the use of injectable materials. DATA SYNTHESIS: Endoscopic injection therapy successfully cures vesicoureteric reflux in 60% to 80% of cases. Success rates are higher with particulate materials (Teflon and Macroplastique) than with bovine collagen or autologous chondrocytes. Long term data regarding cure are scant. Although concerns about particulate migration and autoimmune disease exist, these have not been borne out of clinical experience. Endoscopic injection may be accomplished on an outpatient basis, with less morbidity than with open ureteroneocystostomy. CONCLUSIONS: Endoscopic injection therapy should be offered as an alternative treatment in patients with indications to consider ureteroneocystotomy, but should not change the indications for surgical intervention. The ideal biomaterial for injection has yet to be developed, but the field of autologous tissue engineering holds promise for future development.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".