Retrograde Urethrocystography Impairs Computed Tomography Diagnosis of Pelvic Arterial Hemorrhage in the Presence of a Lower Urologic Tract Injury
Bibliographic record
Abstract
BACKGROUND: There is controversy about the appropriate sequence of urologic investigation in patients with pelvic fracture. Use of retrograde urethrography or cystography may interfere with regular pelvic CT scanning for arterial extravasation. STUDY DESIGN: We performed a retrospective study at a regional trauma center in Toronto, Canada. Included were adult blunt trauma patients with pelvic fractures and concomitant bladder or urethral disruption who underwent initial pelvic CT before operation or hospital admission. Exposure of interest was whether retrograde urethrography (RUG) and cystography were performed before pelvic CT scanning. Main outcomes measures were indeterminate or false negative initial CT examinations for pelvic arterial extravasation. RESULTS: Sixty blunt trauma patients had a pelvic fracture and either a urethral or bladder rupture. Forty-nine of these patients underwent initial CT scanning. Of these 49 patients, 23 had RUG or conventional cystography performed before pelvic CT scanning; 26 had cystography after regular CT examination. Performing cystography before CT was associated with considerably more indeterminate scans (9 patients) and false negatives (2 patients) for pelvic arterial extravasation (11 of 23 versus 0 of 26, p < 0.001) compared with performing urologic investigation after CT. In the presence of pelvic arterial hemorrhage, indeterminate or false negative CT scans for arterial extravasation were associated with a trend toward longer mean times to embolization compared with positive scans (p=0.1). CONCLUSIONS: Extravasating contrast from lower urologic injuries can interfere with the CT assessment for pelvic arterial extravasation, delaying angiographic embolization.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".