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Record W4254053041 · doi:10.1007/978-1-62703-206-3_17

Ureteral Stents

2012· book-chapter· en· W4254053041 on OpenAlexaff
Ben H. Chew, Ryan F. Paterson, Dirk Lange

Bibliographic record

VenueHumana Press eBooks · 2012
Typebook-chapter
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex

Ureteral stents are commonly used in urology to splint an anastomosis or provide drainage of the upper tract. This chapter discusses current stent materials including coatings and drug-elution, the methods used to improve stent symptoms including choosing the correct stent length and potential future technologies including new materials, coatings, and drug-elution. Briefly, there is evidence that stents are not routinely necessary after uncomplicated ureteroscopy. Patients without stents may have significantly less symptoms that those who are stented after ureteroscopy. Infection rates are relatively uncommon and preoperative antibiotics are warranted but there is little evidence for continued antibiotics thereafter. Stent-related biofilms can help create the infection and there are novel coatings and investigations to understand and prevent the development of these biofilms. Almost all patients experience discomfort and stent symptoms. Drug-eluting stents such as ketorolac have been placed into stents to reduce discomfort. There is good evidence that choosing the correct length of stent is most helpful in preventing stent symptoms. Orally administered alpha blockers provide significant reduction in symptoms of the lower tract related to ureteral stents. One day, a new design, possibly coupled with a new biomaterial will help reduce patient symptoms.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.956
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.132
GPT teacher head0.296
Teacher spread0.164 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2012
Admission routes1
Has abstractyes

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