Pearls and Pitfalls in Diagnosing Pediatric Urinary Bladder Masses
Bibliographic record
Abstract
Urinary bladder masses are rare in children, and the associated histologic features and prognoses in this population are different from those in adults. Most children with urinary bladder masses present with lower urinary tract symptoms, which may include hematuria, dysuria, frequent urination, and urgency to urinate. However, some of these masses may be identified incidentally or involve generic symptoms such as abdominal distention. In general, pediatric bladder tumors can be divided into those that originate from the bladder epithelium, known as urothelial neoplasms, and mesenchymal bladder neoplasms, which are more prevalent. The most common bladder malignancy in children is a rhabdomyosarcoma, whereas the most common benign bladder lesion in the pediatric population is a papillary urothelial neoplasm of low malignant potential (PUNLMP). The first-line imaging tool for assessing bladder lesions is ultrasonography, which may be followed by a cross-sectional imaging examination such as computed tomography or magnetic resonance imaging if the origin of the mass is unclear or if distant spread is suspected. Although imaging may enable the radiologist to suggest a differential diagnosis based on lesion location and patient age, tissue biopsy generally is required to identify the exact pathologic entity. This is usually performed at cystoscopy and may be curative in cases in which the lesion is small and has low recurrence potential. Knowledge of the clinical, histopathologic, and imaging features of common bladder neoplasms is essential, as it can aid in preventing imaging pitfalls. These may include the misinterpretation of either a pelvic mass as arising from the bladder or a bladder mass as arising from the pelvis, and interpreting an inflammatory mass or bladder detritus as a neoplasm. ©RSNA, 2017
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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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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".