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Laparoscopically assisted ureterocystoplasty

2003· article· en· W2011599032 on OpenAlexaboutno aff
Bartley G. Cilento, David A. Diamond, C. K. Yeung, Gianantonio Manzoni, Dix P. Poppas, Terry W. Hensle

Bibliographic record

VenueBritish Journal of Urology · 2003
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSurgeryLaparoscopyUreter

Abstract

fetched live from OpenAlex

The concept of ureterocystoplasty, in this case laparoscopically assisted, is addressed by several authors from the USA. The details of their technique are described, and avoiding a large midline incision was found to be particularly beneficial to the patient. Authors from Paris and Toronto describe a further laparoscopic study. They describe a trial comparing the retroperitoneal approach to open partial nephrectomy. These authors also feel that the laparoscopic approach to surgery, in this case partial nephrectomy, is safe and feasible in children, with no increase in operative duration and a shorter hospital stay. Objective To assess the efficacy and safety of laparoscopically assisted ureterocystoplasty (LAU) in children. Patients and methods From 1999 to 2001, five patients (mean age 7 years, range 3.5–13) from four centres underwent LAU with laparoscopic mobilization of the small kidney and upper ureter combined with ureterocystoplasty, with exposure of the bladder through a Pfannenstiel incision. The details and outcomes are reviewed. Results The LAU was successful in all five patients; there were no complications. A large midline incision was avoided and the LAU carried out through the better tolerated and less painful Pfannenstiel incision. Conclusion LAU is an appealing technique that is safe with the added benefit of a reduced abdominal incision and acceptable operative duration. This represents the first published report of LAU.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.268
Teacher spread0.253 · 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 designCase report
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

Citations19
Published2003
Admission routes1
Has abstractyes

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