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Record W4232062496 · doi:10.1097/ju.0000000000000243

This Month in Pediatric Urology

2019· article· en· W4232062496 on OpenAlexaboutno aff
Mark P. Cain

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePediatric urologyHydronephrosisGrading (engineering)Urinary systemGrading scalePyeloplastyUrologyPediatricsSurgeryInternal medicine

Abstract

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You have accessJournal of UrologyThis Month in Pediatric Urology1 Jun 2019This Month in Pediatric Urology Mark P. Cain Mark P. CainMark P. Cain More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000243AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail Urinary Tract Dilation Classification System Reliability There are several grading systems for describing prenatal and neonatal hydronephrosis, each of which has advantages. However, to date no protocol has been shown to be more accurate in predicting the need for surgical intervention or further evaluation of neonates due to the difficulty in defining true obstruction, and the subjective variability among pediatric urologists regarding timing and need for surgical intervention. The Upper Tract Dilation (UTD) classification, created by multidisciplinary consensus, is the most recent system. Despite the perceived improvement in its ability to characterize renal, ureteral and bladder pathology, Nelson et al (page 1186) tested the intra-observer and interobserver reliability of the effectiveness of the UTD classification system in determining the need for surgical intervention in children.1 They evaluated 243 patients for prenatal hydronephrosis using interpretations from 7 physicians from 4 institutions. Complete agreement of UTD score was less than 20%, with overall analysis confirming only fair to moderate inter-user agreement. A subset of 80 cases was re-graded by the same physician, with 50% to 75% intra-user agreement. Although overall inter-user agreement was better with the UTD classification system compared to the Society for Fetal Urology scale, the variability in scoring suggests further improvement is needed for classification of prenatal and postnatal hydronephrosis. Baseline Urinary Imaging in Infants with Spina Bifida The many advances in the care of patients with spina bifida have reduced but not eliminated the risk of chronic kidney disease as a disease related morbidity. As these patients are now living later into adult life, it is critical to understand the baseline renal function and risks for deterioration over time. There has been a continuous debate regarding minimal evaluation and followup studies to evaluate renal function in patients with spina bifida. The UMPIRE protocol, which is one of the largest prospective protocols to study the renal risk factors in patients with spina bifida from birth, was created to determine the optimal urological evaluation of these patients. In this multicenter study Tanaka et al (page 1193) report the initial evaluation of 193 infants born with spina bifida who will be followed with a strict protocol of upper tract imaging.2 Renal ultrasound and functional imaging using dimercaptosuccinic acid nuclear medicine scans were normal in the majority of cases, and only 15% had vesicoureteral reflux early in life. These findings confirm the results of many prior studies but most importantly will provide the basis for prospective analysis of a large patient cohort. Pediatric Renal Transplant Competency and Learning Curve We have very little data to help determine the minimum case volume needed to become technically proficient for almost any procedure in pediatric urological surgery. This information would be useful to identify expectations for surgical exposure during residency and fellowship programs, and to create guidelines for surgical mentoring during the first years in practice. Chua et al (page 1199) from Canada used objective outcomes following pediatric renal transplant to identify the critical case volume for a junior faculty to achieve outcomes equivalent to those of a senior surgeon.3 Their analysis revealed that proficiency is achieved after 17 cases and the procedure is mastered after 26 cases, which is not too different from the published adult data suggesting that 30 cases are necessary to develop expertise. These findings support the need to identify specialized teams for internal training and competency. Also, surgical mentoring may be important early in the surgeon’s career for specialized procedures like pediatric renal transplant, as even at a busy center like Toronto it would take up to 5 to 10 years for the junior surgeon to accumulate an adequate case volume. This type of analysis may be useful to determine minimal case volume necessary for maintaining centers of excellence for rare diseases in the future. References 1. : Interobserver and intra-observer reliability of the Urinary Tract Dilation classification system in neonates: a multicenter study. J Urol 2019; 201: 1186. Link, Google Scholar 2. : Baseline urinary tract imaging in infants enrolled in the UMPIRE Protocol for Children with Spina Bifida. J Urol 2019; 201: 1193. Link, Google Scholar 3. : Competence in and learning curve for pediatric renal transplant using cumulative sum analyses. J Urol 2019; 201: 1199. Link, Google Scholar © 2019 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 201Issue 6June 2019Page: 1027-1028 Advertisement Copyright & Permissions© 2019 by American Urological Association Education and Research, Inc.MetricsAuthor Information Mark P. Cain More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.743
Threshold uncertainty score0.367

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.7430.503

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 head0.010
GPT teacher head0.249
Teacher spread0.239 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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".

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Published2019
Admission routes1
Has abstractyes

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