Rapamycin as an alternative to surgical treatment of subependymal giant cell astrocytomas in a patient with tuberous sclerosis complex
Bibliographic record
Abstract
Tuberous sclerosis complex (TSC) is associated with the potential development of benign hamartomas, including subependymal giant cell astrocytomas (SEGAs). Intracranial hypertension can be caused by SEGAs due to their propensity to block the foramen of Monro. The traditional management approach is to monitor SEGAs with periodic neuroimaging and to resect those that exhibit serial growth and/or cause clinical signs of intracranial hypertension. Recent observations suggest that rapamycin therapy may induce partial regression of SEGAs, therefore providing a potential alternative to resection. The authors present the case of an 8-year-old girl with bilateral SEGAs that led to progressive hydrocephaly and incipient signs of papilledema. Three months after initiating rapamycin therapy, the SEGAs exhibited significant reduction in size (82.6% on the left and 46.7% on the right), and the lesions remained stable 5 months later. Compared with previous case reports, similar or even greater antitumor efficacy was achieved with much lower trough levels of rapamycin (10–15 compared with 3.3–4.5 ng/ml, respectively). The authors discuss various aspects of rapamycin therapy and address unresolved issues that highlight the need for further prospective clinical trials.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".