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Record W2030831457 · doi:10.5489/cuaj.1985

The impact of method of distal ureter management during radical nephroureterectomy on tumour recurrence

2014· article· en· W2030831457 on OpenAlexaffvenueabout
Anil Kapoor, Shawn Dason, Christopher B. Allard, Bobby Shayegan, Louis Lacombe, Ricardo Rendon, Niels-Erik Jacobsen, Adrian Fairey, Jonathan I. Izawa, Peter C. Black, Simon Tanguay, Joseph L. Chin, Alan So, Jean‐Baptiste Lattouf, David Bell, Fred Saad, Darrell Drachenberg, Ilias Cagiannos, Yves Fradet, Abdulaziz Alamri, Wassim Kassouf

Bibliographic record

VenueCanadian Urological Association Journal · 2014
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsUniversity of OttawaUniversity of ManitobaMcGill UniversityUniversity of British ColumbiaWestern UniversityUniversité de MontréalUniversité LavalUniversity of AlbertaDalhousie UniversityMcMaster University
Fundersnot available
KeywordsMedicineUreterCuffUrologySurgeryNephrectomyKidneyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTON: Radical nephroureterectomy for upper tract urothelial carcinoma (UTUC) must include some form of distal ureter management to avoid high rates of tumour recurrence. It is uncertain which distal ureter management technique has the best oncologic outcomes. To determine which distal ureter management technique resulted in the lowest tumour recurrence rate, we analyzed a multi-institutional Canadian radical nephroureterectomy database. METHODS: We retrospectively analyzed patients who underwent radical nephroureterectomy with distal ureter management for UTUC between January 1990 and June 2010 at 10 Canadian tertiary hospitals. Distal ureter management approaches were divided into 3 categories: (1) extravesical tenting for ureteric excision without cystotomy (EXTRAVESICAL); (2) open cystotomy with intravesical bladder cuff excision (INTRAVESICAL); and (3) extravesical excision with endoscopic management of ureteric orifice (ENDOSCOPIC). Data available for each patient included demographic details, distal ureter management approach, pathology and operative details, as well as the presence and location of local or distant recurrence. Clinical outcomes included overall recurrence-free survival and intravesical recurrence-free survival. Survival analysis was performed with the Kaplan-Meier method. Multivariable Cox regression analysis was also performed. RESULTS: A total of 820 patients underwent radical nephroureterectomy with a specified distal ureter management approach at 10 Canadian academic institutions. The mean patient age was 69.6 years and the median follow-up was 24.6 months. Of the 820 patients, 406 (49.5%) underwent INTRAVESICAL, 316 (38.5%) underwent EXTRAVESICAL, and 98 (11.9%) underwent ENDOSOPIC distal ureter management. Groups differed significantly in their proportion of females, proportion of laparoscopic cases, presence of carcinoma in situ and pathological tumour stage (p < 0.05). Recurrence-free survival at 5 years was 46.3%, 35.6%, and 30.1% for INTRAVESICAL, EXTRAVESICAL and ENDOSCOPIC, respectively (p < 0.05). Multivariable Cox regression analysis confirmed that INTRAVESICAL resulted in a lower hazard of recurrence compared to EXTRAVESICAL and ENDOSCOPIC. When looking only at intravesical recurrence-free survival (iRFS), a similar trend held up with INTRAVESICAL having the highest iRFS, followed by ENDOSCOPIC and then EXTRAVESICAL management (p < 0.05). At last follow-up, 406 (49.5%) patients were alive and free of disease. CONCLUSION: Open intravesical excision of the distal ureter (INTRAVESICAL) during radical nephroureterectomy was associated with improved overall and intravesical recurrence-free survival compared with extravesical and endoscopic approaches. These findings suggest that INTRAVESICAL should be considered the gold standard oncologic approach to distal ureter management during radical nephroureterectomy. Limitations of this study include its retrospective design, heterogeneous cohort, and limited follow-up.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.267

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.012
GPT teacher head0.283
Teacher spread0.272 · 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.

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

Citations39
Published2014
Admission routes3
Has abstractyes

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