Inflammatory myofibroblastic tumor: A<scp>multi‐institutional</scp>study from the Pediatric Surgical Oncology Research Collaborative
Bibliographic record
Abstract
Inflammatory myofibroblastic tumor (IMT) is a mesenchymal neoplasm of intermediate malignancy. We describe the largest cohort of IMT patients to date, aiming to further characterize this rare, poorly understood tumor. This is a multi-institutional review of IMT patients ≤39 years, from 2000 to 2018, at 18 hospitals in the Pediatric Surgical Oncology Research Collaborative. One hundred and eighty-two patients were identified with median age of 11 years. Thirty-three percent of tumors were thoracic in origin. Presenting signs/symptoms included pain (29%), respiratory symptoms (25%) and constitutional symptoms (20%). Median tumor size was 3.9 cm. Anaplastic lymphoma kinase (ALK) overexpression was identified in 53% of patients. Seven percent of patients had distant disease at diagnosis. Ninety-one percent of patients underwent resection: 14% received neoadjuvant treatment and 22% adjuvant treatment. Twelve percent of patients received an ALK inhibitor. Sixty-six percent of surgical patients had complete resection, with 20% positive microscopic margins and 14% gross residual disease. Approximately 40% had en bloc resection of involved organs. Median follow-up time was 36 months. Overall 5-year survival was 95% and 5-year event-free survival was 80%. Predictors of recurrence included respiratory symptoms, tumor size and distant disease. Gross or microscopic margins were not associated with recurrence, suggesting that aggressive attempts at resection may not be warranted.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".