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Endoscopic treatment of reflux: management pros and cons

2006· review· en· W2061874385 on OpenAlexaff
Armando J. Lorenzo, Antoine E. Khoury

Bibliographic record

VenueCurrent Opinion in Urology · 2006
Typereview
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsUniversity of TorontoHospital for Sick ChildrenSickKids Foundation
Fundersnot available
KeywordsMedicineContext (archaeology)Intensive care medicineModalitiesPsychological interventionVesicoureteral refluxMedical physicsReflux

Abstract

fetched live from OpenAlex

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
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 head0.123
GPT teacher head0.428
Teacher spread0.305 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations17
Published2006
Admission routes1
Has abstractyes

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