Inflammatory Myofibroblastic Tumours of the Urinary Bladder: Multi-Centre 18-Year Experience
Bibliographic record
Abstract
OBJECTIVE: To review a series of inflammatory myofibroblastic tumours (IMTs) of the urinary bladder in 10 hospitals in Hong Kong. METHODS: A database search in the pathology archives of 10 hospitals in Hong Kong from 1995 to 2013 was performed using the key words 'inflammatory myofibroblastic tumour', 'inflammatory pseudotumour' and 'spindle cell lesion'. Patient characteristics, clinical features, histological features, immunohistochemical staining results and treatment outcomes were reviewed. RESULTS: Nine cases of IMT of the urinary bladder were retrieved. The mean age was 45.4 ± 22.8 years (range 11-78). Eight patients (88.9%) presented with haematuria and 5 patients (55.6%) had anaemia with a mean haemoglobin level of 6.8 ± 1.3 g/dl. Histologically, the majority of patients (77.8%) had a compact spindle cell pattern. Anaplastic lymphoma kinase staining was positive in 75% of cases. During a mean follow-up period of 43.4 months (range 8-94), none of them developed any local recurrence or distant metastasis. CONCLUSIONS: A high index of suspicion of IMT should be maintained for young patients presenting with bleeding bladder tumours and significant anaemia. IMTs of the urinary bladder run a benign disease course, and good prognosis can be achieved after surgical resection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".