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Record W1927576610 · doi:10.5489/cuaj.3015

Intra-corporeal robotic renal auto-transplantation

2015· article· en· W1927576610 on OpenAlexaffvenueabout
Jason Y. Lee, Tarek Alzahrani, Michael Ordon

Bibliographic record

VenueCanadian Urological Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsUniversity of TorontoSt. Michael's Hospital
Fundersnot available
KeywordsMedicineSurgeryGold standard (test)NephrectomyTransplantationPyeloplastyIschemiaKidney transplantationKidneyRadiologyUrinary systemInternal medicine

Abstract

fetched live from OpenAlex

Renal auto-transplantation (RATx) is a suitable option for managing patients with long upper ureteric or pan-ureteric strictures. The current gold standard approach to RATx is a laparoscopic nephrectomy followed by open auto-transplantation. The advent of robotic-assisted laparoscopic surgery has allowed us to apply minimally-invasive techniques to ever-more complex surgical procedures. We present the case of a 38-year-old patient referred to our institution for management of a failed laparoscopic pyeloplasty resulting in a long upper ureteric stricture with complete ureteric obstruction. After complete evaluation, RATx was determined as a suitable management option. Completely intracorporeal right RATx was performed robotically with intraperitoneal cold perfusion. Total operative time was 6.5 hours, with total ischemia time of only 79 minutes (4 minutes warm ischemia, 48 minutes cold ischemia, 27 minutes re-warming time), comparable to the gold standard approach for RATx. To our knowledge, this is the first reported case of a completely intracorporeal robotic RATx in Canada.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.027
GPT teacher head0.240
Teacher spread0.213 · 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 designCase report
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

Citations32
Published2015
Admission routes3
Has abstractyes

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Same venueCanadian Urological Association JournalSame topicRenal and Vascular PathologiesFrench-language works237,207