Does Autologous Blood Injection Following Dextranomer/Hyaluronic Acid Copolymer Implantation in Treating Vesicoureteral Reflux Affect the Microsphere Particle Leakage?
Bibliographic record
Abstract
Objectives: It has been shown that concomitant autologous blood and dextranomer/hyaluronic acid (Deflux®) injection, hydrodistension autologous blood injection technique (HABIT), had a better mound preservation and treatment success compared to the hydrodistension injection technique (HIT) in vesicoureteral reflux (VUR) correction. In this study, we aimed to show microscopically whether the concomitant injection of autologous blood decreases the leakage of Deflux® particles. Methods: Children with VUR who underwent HIT or HABIT between March 2020 and January 2023 were enrolled. Following the completion of the procedure on each ureter, the bladder was irrigated for 3 to 5 min, and the retrieved sample of irrigation fluid was evaluated for dextranomer particle count as “immediate leakage”. A Foley catheter was placed, and a urine sample after 12 h was collected as “early leakage”. Results: A total of 86 children with a median age of 3.0 years (interquartile range = 4.6) were included. Overall, 66 patients underwent HABIT, and 20 children underwent HIT. Rupture was observed in five patients during the procedure, and re-injection was conducted successfully in these cases. Immediate, early, and total particle leakage in the first 12 h of the injection were significantly less in the HABIT group compared to the HIT group. In the regression analysis, only the injection technique (HIT/HABIT) and rupture were significantly associated with the total particle leakage in the first 12 h. Conclusions: Immediate injection of autologous blood into the mound following an endoscopic correction of VUR in children is associated with significantly less Deflux® particle leakage from the injection site regardless of the VUR grade. We hypothesize that a concomitant blood injection into the Deflux® mound will create a blood clot while the needle is kept in situ and help to stabilize the mound and decrease treatment failure by minimizing particle leakage from the injection site.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".