The response of spinal cord ependymomas to bevacizumab in patients with neurofibromatosis Type 2
Bibliographic record
Abstract
OBJECTIVE People with neurofibromatosis Type 2 (NF2) have a genetic predisposition to nervous system tumors. NF2-associated schwannomas stabilize or decrease in size in over half of the patients while they are receiving bevacizumab. NF2 patients treated with bevacizumab for rapidly growing schwannoma were retrospectively reviewed with regard to ependymoma prevalence and response to treatment. METHODS The records of 95 NF2 patients receiving bevacizumab were retrospectively reviewed with regard to spinal ependymoma prevalence and behavior. The maximum longitudinal extent (MLE) of the ependymoma and associated intratumoral or juxtatumoral cysts were measured on serial images. Neurological changes and patient function were reviewed and correlated with radiological changes. RESULTS Forty-one of 95 patients were found to have ependymomas (median age 26 years; range 11-53 years). Thirty-two patients with a total of 71 ependymomas had scans appropriate for serial assessment with a mean follow-up of 24 months (range 3-57 months). Ependymomas without cystic components showed minimal change in MLE. Twelve patients had ependymomas with cystic components or syringes. In these patients, reductions in MLE were observed, particularly due to decreases in the cystic components of the ependymoma. Clinical improvement was seen in 7 patients, who all had cystic ependymomas. CONCLUSIONS Bevacizumab treatment in NF2 patients with spinal cord ependymomas results in a decrease in the size of intratumoral and juxtatumoral cysts as well as adjacent-cord syringes and a decrease in cord edema. This may provide clinical benefit in some patients, although the changes do not meet the current criteria for radiological tumor response.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".