THE USE OF MAGNETIC RESONANCE IMAGING IN THE DIAGNOSIS AND FOLLOWUP OF PEDIATRIC PELVIC RHABDOMYOSARCOMA
Bibliographic record
Abstract
PURPOSE: Previous radiological descriptions of pelvic rhabdomyosarcoma emphasized ultrasonography and computerized tomography (CT). Few reports are available on the use of magnetic resonance imaging (MRI) for diagnosing and following pelvic rhabdomyosarcoma. We retrospectively compared MRI to CT for diagnosing and following children with pelvic rhabdomyosarcoma. MATERIALS AND METHODS: We treated 4 boys and 3 girls for pelvic rhabdomyosarcoma. Initial and followup evaluations included pelvic CT and MRI at intervals determined by treatment and disease status. We retrospectively reviewed the clinical charts and imaging studies of these patients. The initial radiological report was evaluated and then 1 radiologist reviewed all studies. Attention was directed toward identifying lesions revealed by CT or MRI but not by the other modality. RESULTS: MRI detected all lesions shown by CT. On the other hand, MRI detected residual disease in 1 case that was not demonstrated by CT. In 2 other patients MRI was superior to CT for delineating the local extent of disease, especially urethral involvement. CONCLUSIONS: Compared with CT, MRI improves the detection of residual pelvic rhabdomyosarcoma. Tissue planes are well delineated, allowing more accurate assessment of tumor invasion into adjacent structures. MRI is the imaging modality of choice for following pediatric patients with pelvic rhabdomyosarcoma.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".