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Record W2589056863 · doi:10.5489/cuaj.4342

Vesicoureteral reflux: From prophylaxis to surgery

2017· review· en· W2589056863 on OpenAlexaffvenue
Anne‐Sophie Blais, Stéphane Bolduc, Katherine Moore

Bibliographic record

VenueCanadian Urological Association Journal · 2017
Typereview
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsVesicoureteral refluxMedicinePediatric urologyGeneral surgerySurgeryUrologyIntensive care medicineRefluxInternal medicineDisease

Abstract

fetched live from OpenAlex

Vesicoureteral reflux (VUR) is one of the most common pathologies encountered in pediatric urology. Better understanding of the evolution of VUR and new endoscopic surgical techniques in the last decades have led to major changes in the management of this pathology. However, the treatment algorithm remains complex and is composed of a wide variety of options, from active surveillance to surgical treatment. Herein, we propose to review treatment options for VUR in order to help clinicians make the right treatment decision for the right patient.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
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 head0.095
GPT teacher head0.336
Teacher spread0.241 · 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 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

Citations26
Published2017
Admission routes2
Has abstractyes

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