Delayed Iatrogenic Bladder Rupture Diagnosed by POCUS in the Emergency Department
Bibliographic record
Abstract
Bladder rupture is an uncommon injury that leads to significant morbidity and mortality. Though occurring mostly due to trauma, this life-threatening pathology may also occur spontaneously or after a procedure such as transurethral resection of bladder tumor (TURBT). Computed tomography (CT) cystography is the standard imaging modality for diagnosis. However, this test is unlikely to be ordered in a patient with undifferentiated abdominal pain unless there is specific suspicion for this diagnosis. In our emergency department, a 48 year-old male with history of bladder cancer and TURBT two weeks prior to arrival presented with severe abdominal pain and difficulty urinating for 3 days. Point of care ultrasound (POCUS) revealed an irregularly shaped bladder, likely site of bladder rupture, and large amount of abdominal free fluid with sediment. These findings prompted an expedited diagnostic CT scan with cystography. Emergent exploratory laparotomy ultimately confirmed a small bladder defect with 2.5 L of urinary ascites. The diagnosis of non-traumatic bladder rupture can be overlooked in patients presenting with a peritonitic abdominen. The typically ordered test for such patients is standard CT, which carries a high false-negative rate for bladder rupture. This case highlights the utility of POCUS in facilitating a rapid diagnosis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".