Inflammatory myofibroblastic tumors of the central nervous system that express anaplastic lymphoma kinase have a high recurrence rate
Bibliographic record
Abstract
BACKGROUND: Inflammatory myofibroblastic tumors (IMTs) of the central nervous system (CNS) are rare entities with diverse histopathological features and varying propensities to recur. CASE DESCRIPTION: A 26 year-old male with an IMT of the CNS of the left tentorium had tumor progression 2 months after partial surgical resection. Histopathological studies confirmed expression of ALK. Macroscopic total resection was performed followed by radiotherapy. A recurrence occurred 20 months after the second surgery that necessitate reoperation. Including the present case, we identified 30 cases of IMT of the CNS corresponding to our search criteria in the literature. The extent of resection was reported in 26 of these cases. Gross total resection was done in 75% of ALK-positive and in 61% of ALK-negative cases. Recurrence rate after gross total resection for ALK-positive and ALK-negative cases was 33% and 9%, respectively. Every recurrence in ALK-positive patients occurred within 2 years after surgery. CONCLUSION: IMT of the CNS are a heterogeneous group of tumors and the treatment of choice is complete surgical resection. Because of the high recurrence rate reported for IMT of the CNS expressing ALK, a closed follow-up is recommended. When faced with an early recurrence, a surgical resection followed by radiotherapy may be advised.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".