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Record W2316575033 · doi:10.1016/j.juro.2016.02.1805

V4-01 OUR SUCCESSFUL EXPERIENCE UTILIZING POSTERIOR RHABDOSPHINCTER RECONSTRUCTION DURING ROBOTIC PROSTATECTOMY

2016· article· en· W2316575033 on OpenAlexaboutno aff
Mona Yezdani, Ben Katz, Sylvia Yu, daniel maas, Alexa Lee, Alice McGill, Kelly Monahan, David Lee

Bibliographic record

VenueThe Journal of Urology · 2016
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsProstatectomyMedicineNeck of urinary bladderProstateDissection (medical)AnatomySurgeryUrinary bladderCancer

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyRobotics – Prostate/Novel Imaging1 Apr 2016V4-01 OUR SUCCESSFUL EXPERIENCE UTILIZING POSTERIOR RHABDOSPHINCTER RECONSTRUCTION DURING ROBOTIC PROSTATECTOMY Mona Yezdani, Ben Katz, Sylvia Yu, daniel maas, Alexa Lee, Alice McGill, Kelly Monahan, and David Lee Mona YezdaniMona Yezdani More articles by this author , Ben KatzBen Katz More articles by this author , Sylvia YuSylvia Yu More articles by this author , daniel maasdaniel maas More articles by this author , Alexa LeeAlexa Lee More articles by this author , Alice McGillAlice McGill More articles by this author , Kelly MonahanKelly Monahan More articles by this author , and David LeeDavid Lee More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.1805AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES There have been numerous techniques employed to maximize continence after robot-assisted radical prostatectomy (RARP), including posterior rhabdosphincter reconstruction (PRR). However optimum PRR stitch location remains relatively undefined. We aimed to apply our modified posterior reconstruction of the rhabdosphincter (M-PRR) in 570 consecutive patients undergoing RARP for adenocarcinoma of the prostate. In this video, we demonstrate our technique for M-PRR performed during RARP. METHODS This patient had his prostate removed in the standard fashion as previously described. The M-PRR uses the posterior layer of whitish connective tissue that may by reproducibly seen on dissection after division of the posterior bladder wall during the bladder neck transection, and prior to identification of the vasa deferentia and seminal vesicles. This retrotrigonal layer anchor tissue is in clear distinction to Denonvilliers’ fascia, which should not be encountered until after division of the vasa deferentia and seminal vesicles. RESULTS Patient and intraoperative variables were similar between the PRR and M-PRR groups, and there were no surgical technique differences between the two groups. Weeks to 0 PPD was statistically different at the three month interval (p=0.0466). Mean weeks to 1 PPD was 13.1 weeks in the control group and 11.76 weeks in the intervention arm. There were no significant differences in post-operative AUASS or EPIC scores between groups. Console time in this case was 112 minutes. Estimated blood loss was 100 ml. Hospital stay was 26 hours. The foley catheter was removed 6 days after surgery. By also utilizing preoperative kegel exercises, our patient was continent on urethral catheter removal. CONCLUSIONS Retrotrigonal stitch placement during PRR resulted in statistically significant improvement in time to reach total continence post-operatively. Our M-PRR robotic approach for radical prostatectomy is safe and effective. This study is one of the largest to date examining the effect of varied stitch location at the time of RARP on continence outcomes. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e516 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Mona Yezdani More articles by this author Ben Katz More articles by this author Sylvia Yu More articles by this author daniel maas More articles by this author Alexa Lee More articles by this author Alice McGill More articles by this author Kelly Monahan More articles by this author David Lee 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)0.001
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: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.002

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 head0.014
GPT teacher head0.266
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 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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