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PD09-07 RETURNING THE ER: AN ANALYSIS OF EMERGENCY VISITS FOLLOWING UROLOGIC OUTPATIENT PROCEDURES

2020· article· en· W3022171421 on OpenAlexaboutno aff
Luke Witherspoon, Rodney H. Breau, Christopher Knee, Ranjeeta Mallick, J. Stuart Oake, Luke T. Lavallée

Bibliographic record

VenueThe Journal of Urology · 2020
Typearticle
Languageen
FieldMedicine
TopicPelvic and Acetabular Injuries
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineEmergency departmentOutpatient clinicGeneral surgeryFamily medicineMedical emergencyEmergency medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Quality Improvement & Patient Safety II (PD09)1 Apr 2020PD09-07 RETURNING THE ER: AN ANALYSIS OF EMERGENCY VISITS FOLLOWING UROLOGIC OUTPATIENT PROCEDURES Luke Witherspoon*, Rodney Breau, Christopher Knee, Ranjeeta Mallick, J. Stuart Oake, and Luke Lavallee Luke Witherspoon*Luke Witherspoon* More articles by this author , Rodney BreauRodney Breau More articles by this author , Christopher KneeChristopher Knee More articles by this author , Ranjeeta MallickRanjeeta Mallick More articles by this author , J. Stuart OakeJ. Stuart Oake More articles by this author , and Luke LavalleeLuke Lavallee More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000000836.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urologic surgeries have been previously identified as having high rates of readmission compared to other surgeries. This study aims to identify risk factors leading to presentation to the emergency department (ED) following urologic outpatient surgery. METHODS: We examined all outpatient surgeries performed by urology, general surgery, thoracic surgery, and gynecology occurring at three hospitals within The Ottawa Hospital system between April 1, 2008, and February 28, 2018. We captured all ED visits within 90 days of the outpatient procedure. Surgical characteristics included hospital campus, procedure end time, and day/month/year of procedure. Patient characteristics assessed included age, sex, marital status, presence of primary care provider, socioeconomic status (SES), American Society of Anesthesiologists (ASA) score, and Elixhauser comorbidity index. RESULTS: 38,377 outpatient procedures were performed by the four services assessed over our time period. 5641 ED visits within 90 days were identified. Urological procedures accounted for 47.5% (n=2681) of the patients returning to the ED, representing a 16.2% rate of return to the emergency room following urologic day surgery. Furthermore, urologic patients were more likely to have several visits to the emergency room within 90 days compared to other surgical services (p<0.05). Univariable analyses of individual variables found that increased age, low SES, unmarried status were all associated with higher rates of ED visits (p<0.05). The median ER visit day post-procedure ranged from 5-17 depending on procedure type. Of the noted reasons for emergency presentation, the top 5 causes were directly related to urology with retention (11% of visits), and hematuria (10% of visits) being the most common. Of urological day surgery procedures ureteroscopy (Relative Risk (RR) 3.69, 95% CI 2.17,6.29), Greenlight Laser Photoselective Vaporization of the prostate (RR 3.6, 95% CI 2.05, 6.34), and ureteric stent insertion (RR 3.83, 95% CI 2.17, 6.76) had the highest rates of return to emergency in 90 days on multivariate analysis. CONCLUSIONS: ED visits following urologic outpatient procedures are common. This study identifies risk factors to identify patients that may benefit from additional education or support after outpatient urologic surgery to reduce ED care needs. Source of Funding: None © 2020 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 203Issue Supplement 4April 2020Page: e177-e177 Advertisement Copyright & Permissions© 2020 by American Urological Association Education and Research, Inc.MetricsAuthor Information Luke Witherspoon* More articles by this author Rodney Breau More articles by this author Christopher Knee More articles by this author Ranjeeta Mallick More articles by this author J. Stuart Oake More articles by this author Luke Lavallee More articles by this author Expand All Advertisement PDF downloadLoading ...

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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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.053
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.026
GPT teacher head0.302
Teacher spread0.275 · 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
Published2020
Admission routes1
Has abstractyes

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