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Record W2078863227 · doi:10.1503/cjs.003411

Endoscopic treatment of vesicoureteral reflux in children with subureteral dextranomer/hyaluronic acid injection: a single-centre, 7-year experience

2012· article· en· W2078863227 on OpenAlexvenueno aff
Mihovil Biočić, Jakov Todorić, Dražen Budimir, Andrea Cvitković Roić, Zenon Pogorelić, Ivo Jurić, Tomislav Šušnjar

Bibliographic record

VenueCanadian Journal of Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVesicoureteral refluxHyaluronic acidUrologyEndoscopic treatmentRefluxEndoscopyInternal medicineAnatomyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: The goals of medical intervention in patients with vesicoureteral reflux are to allow normal renal growth, prevent infections and pyelonephritis, and prevent renal failure. We present our experience with endoscopic treatment of vesicoureteral reflux in children by subureteral dextranomer/hyaluronic acid copolymer injection. METHODS: Under cystoscopic guidance, dextranomer/hyaluronic acid copolymer underneath the intravesical portion of the ureter in a subureteral or submucosal location was injected in patients undergoing endoscopic correction of vesicoureteral reflux. RESULTS: A total of 282 patients (120 boys and 162 girls) underwent the procedure. There were 396 refluxed ureters altogether. The mean age of patients was 4.9 years. The mean overall follow-up period was 44 months. Among the 396 ureters treated, 76% were cured with a single injection. A second and third injection raised the cure rate to 93% and 94%, respectively. Twenty-two (6%) ureters failed all 3 injections, and were converted to open surgery. CONCLUSION: Endoscopic treatment of vesicoureteral reflux can be recommended as a first-line therapy for most cases of vesicoureteral reflux, because of the short hospital stay, absence of complications and the high success rate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.458

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.242
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2012
Admission routes1
Has abstractyes

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