MétaCan
Menu
← Back to cohort
Record W2096268649 · doi:10.1016/j.juro.2012.02.573

502 THE IMPACT OF SURGEON VOLUME AND SURGICAL APPROACH ON POST-RADICAL PROSTATECTOMY MORBIDITY IN MARYLAND HOSPITALS

2012· article· en· W2096268649 on OpenAlexaboutno aff
Jeffrey K. Mullins, Elias S. Hyams, Phillip M. Pierorazio, Zhaoyong Feng, Bruce J. Trock, Mohamad E. Allaf, Brian R. Matlaga

Bibliographic record

VenueThe Journal of Urology · 2012
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstatectomyGeneral surgeryQuarter (Canadian coin)SurgeryProstate cancerCancerInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyProstate Cancer: Localized II1 Apr 2012502 THE IMPACT OF SURGEON VOLUME AND SURGICAL APPROACH ON POST-RADICAL PROSTATECTOMY MORBIDITY IN MARYLAND HOSPITALS Jeffrey Mullins, Elias Hyams, Phillip Pierorazio, Zhaoyong Feng, Bruce Trock, Mohamad Allaf, and Brian Matlaga Jeffrey MullinsJeffrey Mullins Baltimore, MD More articles by this author , Elias HyamsElias Hyams Baltimore, MD More articles by this author , Phillip PierorazioPhillip Pierorazio Baltimore, MD More articles by this author , Zhaoyong FengZhaoyong Feng Baltimore, MD More articles by this author , Bruce TrockBruce Trock Baltimore, MD More articles by this author , Mohamad AllafMohamad Allaf Baltimore, MD More articles by this author , and Brian MatlagaBrian Matlaga Baltimore, MD More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2012.02.573AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Nationwide data have suggested a link between surgeon volume and post-operative outcomes for men undergoing radical prostatectomy (RP). Furthermore, single surgeon series have suggested improved patient outcomes using robotic technology. However, the interaction of surgeon volume and surgical approach on post-operative morbidity has been less well characterized. The objective of this study is to assess the impact of surgeon volume and surgical approach on post-RP morbidity. METHODS The Maryland Health Service Cost Review Commission (HSCRC) database was queried for men undergoing RRP or RALRP from the fourth calendar quarter of 2008 to the first calendar quarter of 2011 using discharge ICD-9 codes. Patient demographic and immediate post-operative outcomes including length of hospital stay (LOS), hospital re-admission within 30 days, and need for intensive care unit admission were compared between patients undergoing surgery by high volume (>40 cases/year) and low volume surgeons (< 40 cases/year). Multivariable logistic regression analyses were performed to test the association between operative approach and surgeon volume with post-operative outcomes. RESULTS The study cohort consisted of 4,064 men undergoing radical prostatectomy of whom 76.6% had their surgery performed by a high volume surgeon. Patients undergoing RP by a low volume surgeon were more likely to have a robotic operation, be of non-Caucasian ethnicity, have a longer LOS (2.1 vs. 1.7 days, p <0.001), and were more likely to be readmitted to the hospital within 30 days (1.8% vs. 0.13%, p < 0.001). Multivariable logistic regression analyses demonstrated that high surgeon volume was significantly associated with lower risk of LOS > 2 days (OR 0.3, 95% CI: 0.2 - 0.4). Furthermore, open RP was significantly associated with LOS > 2 days (OR 2.3, 95% CI: 1.8 - 3.0) and 30-day readmission (OR 20.6, 95% CI: 2.7 - 154.5). After controlling for surgical approach, high surgeon volume was significantly associated with LOS > 2 days for both robotic and open RP, but the impact of surgeon volume on LOS was significantly greater for open RP. CONCLUSIONS Patients undergoing Robotic RP experienced shorter hospital stays and decreased rates of 30 day re-admission compared to those undergoing open RP. However, surgeon volume decreases post-operative LOS regardless of approach. When considering state-wide data, high surgeon volume and a robotic surgery improve post-operative morbidity. © 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 4SApril 2012Page: e206 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Jeffrey Mullins Baltimore, MD More articles by this author Elias Hyams Baltimore, MD More articles by this author Phillip Pierorazio Baltimore, MD More articles by this author Zhaoyong Feng Baltimore, MD More articles by this author Bruce Trock Baltimore, MD More articles by this author Mohamad Allaf Baltimore, MD More articles by this author Brian Matlaga Baltimore, MD 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)0.007
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 score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0180.001

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 head0.015
GPT teacher head0.288
Teacher spread0.273 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of Urology→Same topicProstate Cancer Diagnosis and Treatment→French-language works237,207→