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Record W2333106326 · doi:10.1016/j.juro.2016.02.1815

V4-11 A NOVEL TRANSVERSUS ABDOMINAL PLANE BLOCK DURING ROBOTIC ASSISTED RADICAL PROSTATECTOMY

2016· article· en· W2333106326 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
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstatectomyLaparoscopic radical prostatectomyAxillary linesLaparoscopyGeneral surgerySurgeryProstate

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyRobotics – Prostate/Novel Imaging1 Apr 2016V4-11 A NOVEL TRANSVERSUS ABDOMINAL PLANE BLOCK DURING ROBOTIC ASSISTED RADICAL 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.2016.02.1815AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Robot assisted radical prostatectomy (RARP) has led to decreased patient morbidity and quicker convalescence. However, narcotic analgesics are still required by many patients and efforts to reduce use have been described. Percutaneous transversus abdominis plane (TAP) block has been well described in the literature to decrease postoperative pain. Classically, TAP block is done at the level of the anterior axillary line between the iliac crest and the costal margin and the analgesic is injected percutaneously through the external oblique, and infused between the internal oblique and transversus abdominis muscles (Figure 1). However, proper injection requires ultrasound guidance to place the medication in the proper layer. Our theory is that transperitoneal laparoscopy can provide easy visualization of the transversus abdominis thus obviating the need for ultrasound. Our goal was to evaluate a novel method utilizing a robotic assisted TAP block on postoperative pain in RARP. METHODS Ninety patients undergoing RARP received 10cc of 0.5% bupivacaine by infiltrating the laparoscopic port sites under our usual protocol (n=50) or a robot assisted TAP block with 10cc of 0.5% bupivacaine (n=40). One patient from each arm was excluded for opioid use preoperatively for chronic pain. Furthermore, all patients received around the clock ketorolac, and as needed oxycodone/acetaminophen, or regular acetaminophen in the postoperative period. All of the patients received standard general anesthetic. After the conclusion of the case, the TAP group received a robot-assisted TAP block of 5cc bilaterally by raising a wheal above the transversus abdominis muscle (Figure 2). Patients were assessed after the operation by a blinded registered nurse at 6 hour intervals until 24 hours after surgery. RESULTS Robot assisted TAP block significantly reduced postoperative adjusted morphine equivalent consumption [mean (SD) 11.9 (13.3) vs. 19.7 (19.1) mg, P=0.0254]. Postoperative pain scale scores were also decreased in the TAP block group for all times with hours 6-12 postop being statistically significant [P=0.0075]. There were no adverse reactions attributable to the TAP block. CONCLUSIONS We have demonstrated a novel robot-assisted TAP block which shows considerable promise in not only decreasing our patients’ pain levels, but also reducing narcotic reliance and potentially avoiding undue deleterious effects. We have also simplified the technique by obviating the use of ultrasound via the direct visualization the laparoscopic approach provides. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e520 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)0.000
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 score0.004
Threshold uncertainty score0.014

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.018
GPT teacher head0.243
Teacher spread0.225 · 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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Citations1
Published2016
Admission routes1
Has abstractyes

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