MétaCan
Menu
Back to cohort

Imaging of urethral stricture disease.

2015· review· en· W2173765112 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePubMed · 2015
Typereview
Languageen
FieldMedicine
TopicUrological Disorders and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineUrethral strictureVoiding cystourethrogramCystoscopyMagnetic resonance imagingRadiologyUrethraGold standard (test)SurgeryDiseaseVesicoureteral refluxAnatomyPathology

Abstract

fetched live from OpenAlex

Accurate imaging of urethral strictures is critical for preoperative staging and planning of reconstruction. The current gold standard, retrograde urethrography (RUG), allows for accurate diagnosis, staging, and delineation of urethral strictures, and remains a cornerstone in the management of urethral stricture disease. In complex situations, the RUG can be combined with voiding cystourethrogram (VCUG) in order to better visualize the posterior urethra or complex distraction defects. Direct visualization of the stricture by cystoscopy, either retrograde or antegrade, can provide additional information as to the location and appearance of stricture, as well as precise location on fluoroscopic imaging. Sonourethrography (SU) is a useful adjunct to allow for three-dimensional assessment of stricture length and location, and can be a useful intraoperative assessment tool, however, its use remains limited to a second-line setting. Cross-sectional imaging in the form of computed tomography (CT) or magnetic resonance urethrography can provide additional three-dimensional information of anatomic structures and their relations, and can serve as a useful adjunct in complex clinical scenarios.

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.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.531

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.089
GPT teacher head0.326
Teacher spread0.237 · 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