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PD57-12 ROLE OF ROUTINE BIOPSY AFTER RADIATION-BASED THERAPY FOR MUSCLE-INVASIVE BLADDER CANCER

2019· article· en· W2941696086 on OpenAlexaboutno aff
Ronald Kool, Adnan El‐Achkar, Gautier Marq, Leonardo L. Monteiro, Marie Vanhuyse, Armen Aprikian, Simon Tanguay, Fabio Cury, Luís Souhami, Wassim Kassouf

Bibliographic record

VenueThe Journal of Urology · 2019
Typearticle
Languageen
FieldMedicine
TopicBladder and Urothelial Cancer Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBladder cancerBiopsyCystectomyRadiation therapyCancerCystoscopyGeneral surgerySurgeryRadiologyInternal medicineUrinary system

Abstract

fetched live from OpenAlex

INTRODUCTION AND OBJECTIVES: Mortality of Radical cystectomy (RC) differs in the literature from as low as 2.3% for a 90day mortality in a single institution, to as high as 7.9% for a prospective multi-institutional series or 5% for a retrospective review of administrative population-based database.90-day mortality rate of RC in a nation-wide population-based study has not been explored.The aim of this study is to investigate 90-day mortality rate of RC for bladder cancer in a nation-wide population-based study, and to evaluate the effect of number of RC per hospital on the surgical outcomes.METHODS: We used mandatory hospital discharge forms (CMBD) of all patients submitted to RC due to bladder cancer in Spain during 2011-2015.Morbidity and mortality were assessed using discharge codes of the primary admission or any other registered admission up to 3 months after the procedure.Demographics of patients including age, sex, and Charlson comorbidity score as well as hospital size and number of RCs/year have also been recorded.We calculated in-hospital, 30-, 60-and 90-day mortality.Average annual RC volume was used as a continuous variable (logtransformed) and also grouped into deciles in order to identify any potential non-linear relationships.Logistic regression model with mixed effect was performed adjusting for year of surgery, comorbidity, surgical approach, type of admission, age, sex, and hospital size.RESULTS: A total of 12154 RC were operated on in 196 hospitals.87.2% of the patients were males, mean age was 68.1 years (SD 9.8).88.9% of the cases received open surgery, 10% laparoscopic surgery and 1.2% robot-assisted surgery.Most hospitals (110) performed <[ 10 RC/year whereas only 5 did more than 38/year.30-, 60-and 90-day mortality rates of the series were 2.9%, 5.1% and 6.5%, respectively.Lowest mortality rates (3.3% at 90 days) are achieved in hospitals doing more than 38 cases per year.The 90-day adjusted mortality rate is associated with annual average RC volume with a 20.6% decrease per 10 extra RCs /year (95% CI 12.3%-28.1%p<0.001).High Charlson comorbidity index, advanced age, and open surgical approach were the clinical variables associated with higher mortality.CONCLUSIONS: In the setting of a nation-wide populationbased study we report a mortality rate comparable to previous multiinstitutional studies.Our study identifies an inverse association between 90-day mortality and hospital volume.The lack of centralization for RC is of concern in that low-volume centers have a mortality higher than high-volume centers.This would have a more pronounced benefit for patients at high-risk.

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)0.002
metaresearch head score (Gemma)0.007
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 score0.074
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0740.026

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 head0.277
Teacher spread0.265 · 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
Published2019
Admission routes1
Has abstractyes

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