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Record W4320151517 · doi:10.5114/polp.2022.123914

A new insight into the management of high-grade vesicoureteral reflux

2022· article· en· W4320151517 on OpenAlexaff
Mohamed G. Atta, Asmaa Ismail, Ahmed Kotb

Bibliographic record

VenuePediatria Polska · 2022
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsNOSM UniversityThunder Bay Regional Health Sciences Centre
Fundersnot available
KeywordsVesicoureteral refluxMedicineAttaRefluxInternal medicineZoology

Abstract

fetched live from OpenAlex

Introduction High-grade vesicoureteral reflux (VUR) is a major dilemma to urologists, with no clear time for intervention in children without evidence of urinary tract infection. The aim of our study was to show our experience in managing high-grade VUR in children with high postvoiding residual. Material and methods Our study included 24 children with high-grade reflux persistent while being on surveillance and continuous antibiotic prophylaxis. Besides radiological investigations, 24-hour urine output and postvoiding residual were our main clinical parameters to study. Results All the children with high-grade reflux in our study were found to have polyuria and high postvoiding residual. Surgical correction was done through open surgical ureteral reimplantation combined with the use of vasopressin. Reduction cystoplasty was done for 2 children. One-year follow-up showed satisfactory outcomes in reducing bladder capacity and treating reflux. Conclusions Children with high-grade VUR should be assessed for polyuria. The presence of polyuria should be an indication for early surgical management.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.015
GPT teacher head0.267
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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