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Record W2795815834 · doi:10.1016/j.juro.2018.02.181

MP05-10 UPDATE ON THE LARGEST CANADIAN RARP EXPERIENCE: ONCOLOGICAL AND FUNCTIONAL OUTCOMES OF 1034 RARP CASES WITH 6-YEAR FOLLOW-UP

2018· article· en· W2795815834 on OpenAlexaboutno aff
Côme Tholomier, Félix Couture, Marc Zanaty, Khaled Ajib, Assaad El‐Hakim, Kevin C. Zorn

Bibliographic record

VenueThe Journal of Urology · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstatectomyInterquartile rangeProstate cancerGeneral surgeryCancerSurgeryInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyProstate Cancer: Localized: Surgical Therapy I1 Apr 2018MP05-10 UPDATE ON THE LARGEST CANADIAN RARP EXPERIENCE: ONCOLOGICAL AND FUNCTIONAL OUTCOMES OF 1034 RARP CASES WITH 6-YEAR FOLLOW-UP Côme Tholomier, Félix Couture, Marc Zanaty, Khaled Ajib, Assaad El-Hakim, and Kevin C. Zorn Côme TholomierCôme Tholomier More articles by this author , Félix CoutureFélix Couture More articles by this author , Marc ZanatyMarc Zanaty More articles by this author , Khaled AjibKhaled Ajib More articles by this author , Assaad El-HakimAssaad El-Hakim More articles by this author , and Kevin C. ZornKevin C. Zorn More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.181AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES RARP (robotic-assisted radical prostatectomy) is now the predominant surgical approach to treat localized prostate cancer. However, there is still limited Canadian data on its outcomes. We herein update the largest RARP experience in Canada, focusing on oncological and functional outcomes. METHODS Prospective data from 1034 cases of RARP performed by two staff surgeons (KCZ, AE) at a single academic center were collected from October 2016 to June 2017. Pre-operative characteristics and post-operative surgical, pathological, functional and oncological outcomes were assessed up to 72 months postoperative. RESULTS Median follow-up (interquartile range) was 30 months (12-48). D’Amico risk distribution was 26.1% low, 59.8% intermediate and 14.1% high-risk. Median operative time was 170 minutes (145-200), blood loss was 200 mL (150-300) and the postoperative hospital stay was 1 day (1-1). Transfusion rate was only 1.4%. Intraoperative complications rate was 3.8%. There was a total of 32 (3.1%) major post-operative complications (Clavien III-IV) and 138 (13.3%) minor complications (Clavien I-II). 630 patients were staged pT2 (64.2%) with a positive surgical margin (PSM) rate of 15.7% (99). 349 patients were staged pT3 (35.6%), of which 39.0% (136) had a PSM. 1 patient was staged pT4. Urinary continence (defined as 0 pads/day) returned at 3, 6, 12 and 24 months for 71.9%, 82.1%, 88.5% and 91.2% of patients, respectively. Potency rates (defined as successful penetration) was 25.3%, 32.0%, 43.5% and 50.1% at 3, 6, 12 and 24 months, respectively. Biochemical recurrence occurred in 114 patients (11.0%). 46 patients (4.4%) had hormonotherapy while 136 patients (13.2%) were referred for salvage radiotherapy. CONCLUSIONS This updated study shows comparable results to other high-volume centers. RARP appears to be safe with acceptable surgical, oncological and functional outcomes. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e46 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Côme Tholomier More articles by this author Félix Couture More articles by this author Marc Zanaty More articles by this author Khaled Ajib More articles by this author Assaad El-Hakim More articles by this author Kevin C. Zorn 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)0.004
metaresearch head score (Gemma)0.015
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 score0.085
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.003

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 head0.034
GPT teacher head0.286
Teacher spread0.252 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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