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Record W2911505359 · doi:10.4293/jsls.2018.00085

Improved Outcomes Utilizing a Valveless-Trocar System during Robot-assisted Radical Prostatectomy (RARP)

2019· article· en· W2911505359 on OpenAlexfundno aff
Mohammed Shahait, Ross Cockrell, Mona Yezdani, Sue-Jean Yu, Alexandra Lee, Kellie McWilliams, David I. Lee

Bibliographic record

VenueJSLS Journal of the Society of Laparoscopic & Robotic Surgeons · 2019
Typearticle
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsnot available
FundersMcGill UniversityUniversity of Pennsylvania
KeywordsMedicineNauseaProstatectomyVomitingBlood lossSurgeryAnesthesiaComplicationBody mass indexLaparoscopic radical prostatectomyProspective cohort studyUrologyInternal medicineProstateCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: To evaluate the effect of valveless trocar system (VTS) on intra-operative parameters, peri-operative outcomes, and 30-day postoperative complications in patients undergoing robotic-assisted laparoscopic prostatectomy. METHODS: A total of 200 consecutive patients undergoing Robot-assisted radical prostatectomy by a single surgeon were prospectively evaluated using either the valveless trocar (n = 100) or standard trocars (n = 100). Patient demographics, intra-operative parameters, length of stay, presence or absence of postoperative nausea and vomiting, analog pain score at 0-6 hours, 6-12 hours, 12-18 hours, and >24 hours, and 30-day postoperative complications were analyzed. RESULTS: = 0.049), respectively. CONCLUSION: The use of a valveless trocar system during robot-assisted robotic prostatectomy may shorten operative times, and reduce postoperative pain scores and nausea episodes without increasing the 30-day complication rate. Further prospective randomized trials should be performed to validate these findings.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0010.000

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 head0.013
GPT teacher head0.264
Teacher spread0.251 · 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

Citations33
Published2019
Admission routes1
Has abstractyes

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Same venueJSLS Journal of the Society of Laparoscopic & Robotic SurgeonsSame topicIntraoperative Neuromonitoring and Anesthetic EffectsFrench-language works237,207