Bibliographic record
Abstract
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".