NFB-12. Effect of trametinib on leg length discrepancy in a child with NF1 related plexiform neurofibroma
Bibliographic record
Abstract
Abstract INTRODUCTION: Plexiform neurofibroma(PN) is a challenging benign tumor. Recently, MEK inhibitors (MEKi) showed encouraging tumor response. We report the observed effect of Trametinib on leg length discrepancy (LLD) in a child with NF1. CASE DESCRIPTION: A 4 year old girl with sporadic NF1, developed progressive bilateral L1-L5 paraspinal PN extending to the left thigh resulting in hypertrophy of left leg and associated with LLD. At 33 months of age, length difference of 2.8 cm between both femurs was described on scanogram with a projected LLD of at 6.1- 6.2 cm LLD at bone maturity using the multiplier method, a common method of predicting LLD. At 36 months of age, treatment with Trametinib was initiated for her large PN. Ten months into therapy, parents reported impression of decrease swelling of her left thigh enlargement. MRI evaluation showed stable measurement of PN using the RECIST criteria. Repeat measurement on scanogram at 46 months of age disclosed a stable difference of 2.8 cm between both femurs, with a LLD projected at 5.2-5.3 cm at maturity by multiplier method. Bone age at study entry and at 11 months into therapy (Greulich-Pyle) was reported normal for chronologic age. DISCUSSION/CONCLUSION: LLD has not been commonly described in association with NF1 related PN. Given the PN involved mainly the left thigh in our patient, it is reasonable to suggest common underlying mechanism for the PN and faster growth of her left femur. Although with limited time point’s measurements, the early observation of stabilization of the LLD, 10 months into Trametinib therapy suggesting a final discrepancy in femurs length less than initially predicted, is encouraging. Further evaluation at completion of treatment and on follow-up are needed. Larger case series will be useful to explore this unexpected and possible clinical effect of MEKi in NF1 children.
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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 | 0.000 | 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".