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Record W2809604403 · doi:10.5489/cuaj.5381

A survey of Canadian renal transplant surgeons: Use of ureteric stents and technique of the ureteroneocystotomy

2018· article· en· W2809604403 on OpenAlexaffvenueabout
Luke F. Reynolds, Tad Kroczak, R. John D’A. Honey, Kenneth T. Pace, Jason Y. Lee, Michael Ordon

Bibliographic record

VenueCanadian Urological Association Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsUniversity of TorontoToronto General HospitalSt. Michael's Hospital
Fundersnot available
KeywordsMedicineStentUreteric stentSurgeryAnastomosisUreterRenal transplantTransplantation

Abstract

fetched live from OpenAlex

INTRODUCTION: The role of ureteric stenting in renal transplant has been well-demonstrated. The goal of this survey was to determine the utilization of ureteric stents by Canadian transplant surgeons, and how the ureteroneocystotomy and followup is performed. METHODS: An online survey was sent to the 40 surgeon members of the Canadian Society of Transplantation. The primary outcome was the rate of ureteric stent use at the time of renal transplantation. The secondary outcomes were the ureteric stent dwell time, use and type of prophylactic antibiotics, and the use of routine post-transplant ultrasonography. RESULTS: All respondents (25) used ureteric stent routinely and 92% remove the stent between four and six weeks postoperatively. Prophylactic antibiotics were used 64% of the time for ureteric stent removal. The majority of surgeons do not routinely perform a post-stent removal ultrasound. Fifty-six percent of respondents perform a refluxing anastomosis. CONCLUSIONS: Ureteric stents are routinely used in renal transplant in Canada. Areas for improvement and topics of debate identified from this survey are the need for peri-stent removal antibiotics, the role of post-stent removal ultrasound, the duration of stent dwell time, and the need for a non-refluxing ureteroneocystotomy.

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 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 score0.026
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.040
GPT teacher head0.231
Teacher spread0.190 · 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".

Quick stats

Citations2
Published2018
Admission routes3
Has abstractyes

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