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Record W2063993009 · doi:10.5489/cuaj.1392

Robot-assisted right ureteral polypectomy: A case report

2013· article· en· W2063993009 on OpenAlexvenueno aff
Kenneth Lim, Richard A. Santucci, Sabry Mansour

Bibliographic record

VenueCanadian Urological Association Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicUrologic and reproductive health conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePolypectomyLaparoscopyFibroepithelial PolypSurgeryEndometrial PolypUreterGeneral surgeryHysteroscopyColorectal cancerColonoscopyCancerInternal medicine

Abstract

fetched live from OpenAlex

Ureteral polyps are a rare cause of ureteral obstruction in the adult and pediatric populations. Fibroepitheial polyps (FEP) are the most common type of ureteral polyps. This clinical entity is very rare, warranting periodic clinical review by practitioners, and new advancements in laparoscopy allow new surgical approaches to its cure. We present the case of a 20-year-old male with right-sided flank pain. He was found to have right ureteropelvic junction (UPJ) obstruction and subsequently underwent laparoscopic robotic-assisted right collecting system exploration, excision of polyps and right ureteropyeloplasty. Ureteral polyps were excised and determined to be fibroepithelial in origin based on the pathological report. Our case highlights the importance of having FEP in the differential diagnosis of ureteral obstruction. We also found that laparoscopic robot-assisted polypectomy is a safe and acceptable surgical option for the excision of ureteral polyps.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.002
Science and technology studies0.0040.002
Scholarly communication0.0030.003
Open science0.0020.003
Research integrity0.0090.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.259
Teacher spread0.242 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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