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Record W1606172826 · doi:10.1016/j.juro.2014.02.2128

PD30-05 CURRENT PERSPECTIVES OF UROLOGY INVOLVEMENT IN RENAL TRANSPLANTATION: A SURVEY OF CANADIAN SENIOR RESIDENTS

2014· article· en· W1606172826 on OpenAlexaffabout
Jennifer Bjazevic, Thomas McGregor

Bibliographic record

VenueThe Journal of Urology · 2014
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedicineUrologyTransplantationCurrent (fluid)Intensive care medicineInternal medicine

Abstract

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You have accessJournal of UrologyTransplantation & Vascular Surgery I1 Apr 2014PD30-05 CURRENT PERSPECTIVES OF UROLOGY INVOLVEMENT IN RENAL TRANSPLANTATION: A SURVEY OF CANADIAN SENIOR RESIDENTS Jennifer Bjazevic and Thomas McGregor Jennifer BjazevicJennifer Bjazevic and Thomas McGregorThomas McGregor View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.2128AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Medical advancements in transplantation have lead to increasing complexity of the field, and further surgical specialization. Consequently, the role of urology in renal transplantation has become highly variable with the growth of surgeons specialized in multi-organ transplant. However, renal transplantation remains a mandatory component of residency training, as determined by the Royal College of Physicians and Surgeons of Canada. We determined the involvement of urology faculty and residents in renal transplantation, and perceptions of the role of urology in transplantation across Canada. METHODS An anonymous questionnaire was administered to all thirty-one final-year Canadian urology residents at the Queen’s Urology Examination Skills Training program (QUEST). The survey was devised to assess urological involvement and resident exposure to renal transplantation. Responses were closed ended and utilized a validated five-point Likert scale. Descriptive statistics and Pearson’s chi-squared test were used to analyze the responses and demonstrate correlations. RESULTS All residents completed the survey. Urologists were involved in performing renal transplant surgery at most training centers across Canada (77.4%). The majority of residents believed that urology should remain highly involved with transplant (77.4%), and that it should be a mandatory component of residency training (64.5%). There was a positive correlation between the involvement of urology in renal transplantation at a resident’s training centre, and the opinion that urology should continue to play an important role in this field (r=0.51, p=0.003). However, barely half of the residents (51.6%) felt they had sufficient exposure to transplant surgery. Only 41.9% would feel comfortable performing transplant surgery after residency, and these residents were involved in an average of 30 transplant surgeries and 16 laparoscopic donor nephrectomies. A minority of residents had plans for fellowship training (9.7%) or future careers (12.9%) involving renal transplant. CONCLUSIONS Renal transplantation remains a limited component of the majority of residency training programs in Canada. However, the number of residents intending to pursue fellowship training or a future career that involves transplant remains limited. Consequently, a strong exposure to renal transplant during urology residency training is vital to ensuring urology remains highly involved in renal transplantation. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e775-e776 Advertisement Copyright & Permissions© 2014Metrics Author Information Jennifer Bjazevic More articles by this author Thomas McGregor 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)0.001
metaresearch head score (Gemma)0.004
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 score0.116
Threshold uncertainty score0.234

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.281
Teacher spread0.257 · 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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Citations2
Published2014
Admission routes2
Has abstractyes

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