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Record W2313979582 · doi:10.1016/j.juro.2016.02.739

MP23-17 IMPROVED OUTCOMES DURING ROBOTIC PROSTATECTOMY UTILIZING AIRSEAL TECHNOLOGY

2016· article· en· W2313979582 on OpenAlexaboutno aff
Mona Yezdani, Sue-Jean Yu, Alexandra Lee, Benjamin Taylor, Alice McGill, Kelly Monahan, David I. Lee

Bibliographic record

VenueThe Journal of Urology · 2016
Typearticle
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsPneumoperitoneumMedicineInsufflationProstatectomyGeneral surgeryGold standard (test)Medical physicsSurgeryLaparoscopyRadiology

Abstract

fetched live from OpenAlex

You have accessJournal of UrologySurgical Technology & Simulation: Instrumentation & Technology I1 Apr 2016MP23-17 IMPROVED OUTCOMES DURING ROBOTIC PROSTATECTOMY UTILIZING AIRSEAL TECHNOLOGY Mona Yezdani, Sue-Jean Yu, Alexandra Lee, Benjamin Taylor, Alice McGill, Kelly Monahan, and David Lee Mona YezdaniMona Yezdani More articles by this author , Sue-Jean YuSue-Jean Yu More articles by this author , Alexandra LeeAlexandra Lee More articles by this author , Benjamin TaylorBenjamin Taylor 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.2016.02.739AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Airseal is a newer technology utilizing an integrated access system during minimally invasive surgery. Its goal is to provide stable pneumoperitoneum and continuous smoke evacuation. A few small volume studies have compared Airseal to the standard multi-component insufflation system and have shown an improvement in stable pneumoperitoneum and ease of manipulating objects through the Airseal port. In this study, we compare the standard system to the Airseal system to evaluate potential benefits in a larger cohort. METHODS We performed a single-institution, single-surgeon prospective study of 149 consecutive patients who underwent robotic prostatectomy from June 2014 to April 2015. Gas insufflation with CO2 was performed using either standard multi-component insufflation with a 12mm Covidien Versaport bladeless trocar from June 2014 to October 2014 or with Airseal system from November 2014 to April 2015. Multiple data points were assessed including total operative time, estimated blood loss, length of stay, and pain score at 0-6 hours, 6-12 hours, 12-18 hours. RESULTS 149 patients were analyzed with 79 in the control arm and 70 in the study arm. There was no significant difference between the study and control groups in mean age (62 vs. 61) or BMI (28 vs. 27). A significant difference was seen in total operative time with 146 minutes in the Airseal group and 167 minutes in the control (p=0.0002) and in intraoperative blood loss with mean of 132 ml in Airseal group versus 215 ml in the control (p=.0031). Pain scores for time 6-12 hours were significantly lower (3.3 vs. 4.1) in the Airseal group compared to the control but were not significant for 0-6 or 6-18 hours (1.9 vs. 2.4 and 2.9 vs. 3.6, respectively). However, across all times, the numerical level given for pain was always less with Airseal. CONCLUSIONS This prospective study shows an advantage to using Airseal compared to standard insufflation. There is significantly less operative time, intraoperative blood loss, and pain scores at 6-12 hours. This is most likely attributable to the stable pneumoperitoneum and improved visibility without the need for bedside interruption with suction or cleaning of the camera. Improved pain scores may be associated with the stable pneumoperitoneum without intermittent stretching of the muscles and incisions. Thus, the results of this study show that Airseal can be advantageous during robotic prostatectomy. Further larger volume studies are required to assess for the utility of Airseal in all robotic procedures. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e268 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Mona Yezdani More articles by this author Sue-Jean Yu More articles by this author Alexandra Lee More articles by this author Benjamin Taylor 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 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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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".

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Citations0
Published2016
Admission routes1
Has abstractyes

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