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Record W1811601526 · doi:10.5489/cuaj.2319

Technical feasibility of robot-assisted laparoscopic radical prostatectomy in renal transplant recipients: Results of a series of 12 consecutive cases

2015· article· en· W1811601526 on OpenAlexvenueno aff
Q. Le Clerc, Emilie Lecornet, Grégoire Léon, J. Rigaud, Pascal Glémain, Julien Branchereau, Georges Karam

Bibliographic record

VenueCanadian Urological Association Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineProstatectomyPerioperativeUrologySurgeryRenal transplantCreatinineNephrologyProstateTransplantationInternal medicineCancer

Abstract

fetched live from OpenAlex

INTRODUCTION: We evaluate the technical feasibility of robotic prostatectomy in renal transplant recipients. METHODS: We retrospectively analyzed preoperative and perioperative settings, as well as functional and oncologic results of 12 patients operated on between 2009 and 2013. Prostatectomy was performed via a transperitoneal approach without any changing in the ports position. The average age was 61.92 ± 2.98 years. The period between transplant and the diagnosis of adenocarcinoma was 79.7 months. The mean PSA was 7.34 ng/mL (range: 4.9-11). RESULTS: The operative time was 241.3 ± 35.6 minutes with only one conversion and one transfusion. The intervention was difficult due to adhesions on the side of the graft in 50% of cases. There was a case of obstructive acute renal failure resulting from a hematoma of the Retzius treated by percutaneous nephrostomy at D20. There was a majority of pT2c (72.7%), including 3 positive margins (27.3%) and 2 biochemical relapses treated with radiotherapy and hormonotherapy, respectively. The end point prostate-specific antigen was undetectable. There was no significant difference between preoperative and J7 creatinine (p = 0. 22). CONCLUSIONS: Robotic prostatectomy in renal transplant recipients is a safe technique with no serious effects on the allograft.

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)0.002
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 score0.010
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.048
GPT teacher head0.287
Teacher spread0.238 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

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