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MP66-01 EFFECT OF COMPLICATIONS ON UROLOGY TRAINEES: AN EPIDEMIOLOGICAL SURVEY

2023· article· en· W4360606108 on OpenAlexaboutno aff
William Du Comb, Brittany Milliner, Joshua Palka, Kristina D. Suson

Bibliographic record

VenueThe Journal of Urology · 2023
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAccreditationEpidemiologyFamily medicineQuarter (Canadian coin)General surgeryUrologyMedical educationInternal medicine

Abstract

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You have accessJournal of UrologyCME1 Apr 2023MP66-01 EFFECT OF COMPLICATIONS ON UROLOGY TRAINEES: AN EPIDEMIOLOGICAL SURVEY William Du Comb, Brittany Milliner, Joshua Palka, and Kristina Suson William Du CombWilliam Du Comb More articles by this author , Brittany MillinerBrittany Milliner More articles by this author , Joshua PalkaJoshua Palka More articles by this author , and Kristina SusonKristina Suson More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003329.01AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Death, taxes and surgical complications are all guarantees for urologists. Trainees are taught to diagnose and operate; however, little formal education is provided on coping with surgical complications. As such, we also sought to assess how prepared trainees feel to handle complications upon completion of their programs. METHODS: After obtaining IRB approval, an electronic survey was distributed to all ACGME accredited urology programs via email. The survey consisted of 15 questions regarding surgical complications. RESULTS: Overall, 106 residents and fellows responded. Average age of responders was 31 years old. Senior level residents (PGY4-6) comprised the majority (58.5%) of responses. Junior residents (PGY2-3) and interns (PGY1) represented 29.3% and 8.5% of respondents, respectively. Fellows accounted for 8.5% of responses. Discussion of the case among peers was the main coping mechanism (98.1%), while 83% stated they discussed the case with faculty. Over a quarter (26.4%) of trainees used alcohol to cope with their complication, and 38.7% utilized exercise. Thirty percent of trainees were uncertain or denied existence of a mental health support system at their institution. Surgical complications affected trainees by causing performance anxiety (70%), loss of confidence (70.7%), and excessive thoughts regarding the complication (70%). Trainees also expressed emotional damage including feelings of anxiety/fear (72%), sadness/grief/depression (68%), and feelings of being overwhelmed, helpless, or hopeless (51.5%). Just over half of responders lost sleep over their complications. Thirty-seven percent of trainees felt they were unsure or did not feel prepared to handle complications upon graduation. CONCLUSIONS: Although complications are inevitable for urologists, residents may suffer psychologic consequences when they occur and often feel unsure/unprepared for managing them upon graduation. Further research into didactic complication preparation is warranted. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e931 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information William Du Comb More articles by this author Brittany Milliner More articles by this author Joshua Palka More articles by this author Kristina Suson More articles by this author Expand All 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)0.002
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 score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.219
GPT teacher head0.519
Teacher spread0.301 · 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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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