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Record W2791880936 · doi:10.5489/cuaj.4857

Spotlight — Management of pyelovesical bypass device stones

2018· article· en· W2791880936 on OpenAlexaffvenue
Ahmad Almarzouq, Sero Andonian

Bibliographic record

VenueCanadian Urological Association Journal · 2018
Typearticle
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineUreteroscopySurgeryUreterLithotripsy

Abstract

fetched live from OpenAlex

Pyelovesical bypass devices or artificial ureters have been described as a last resort in patients with long ureteral strictures that fail traditional endoscopic and open repair. Herein, we describe a 52-year-old female who had a Detour (Coloplast, Humlebaek, Denmark) pyelovesical bypass device inserted after an iatrogenic ischemic injury to the distal two-thirds of the left ureter during pelvic surgery for recurrent endometrial stromal sarcoma. Six months after placement of the device, she presented with gross hematuria and recurrent urinary tract infections (UTIs) and was found to have encrustation of the distal silicone tip of the Detour device within the bladder. This was managed with resection of the distal silicone tip and flexible ureteroscopy with holmium laser lithotripsy. Despite suppressive antibiotic therapy and medical therapy for hypercalciuria, she presented four years later with intraluminal encrustations in the proximal end of the device. This was successfully managed with flexible ureteroscopy with holmium laser lithotripsy. Therefore, this case illustrates the feasibility of flexible ureteroscopy and holmium laser lithotripsy of Detour device encrustations as long as the device is not kinked and it allows the passage of the flexible ureteroscope up to the calcifications. In addition, patients contemplating insertion of such devices should be counselled regarding the risk of recurrent infections and encrustations.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.081
Threshold uncertainty score0.752

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.016
GPT teacher head0.256
Teacher spread0.240 · 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

Citations3
Published2018
Admission routes2
Has abstractyes

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