P.083 Trigeminal neuralgia caused by an intra-axial glioma in a child: first surgical case report and systematic review
Bibliographic record
Abstract
Background: Trigeminal neuralgia (TGN) is usually caused by arterial compression of the trigeminal nerve. There are very few cases caused by intra-axial brainstem masses. Little information is therefore available regarding the response to incomplete resection of offending lesions. We present one such case, and systematically review the published in the literature. Methods: Case report and systematic review of MEDLINE and EMBASE Results: Case report: A seven year-old girl is referred with typical TGN pain. MRI revealed a cervicomedullary tumor with no abnormalities of the pons or trigeminal nerve. Subtotal resection under neurophysiologic monitoring was achieved, leaving a small residual attached near the expected location of the trigeminal spinal nucleus and tract.Patient recovered well with resolution of her TGN pain. She is asymptomatic seven years post-operatively. Literature Review: We found no other published cases in children or secondary to gliomas. Among reviewed cases, only two underwent surgery. Both were adults with brainstem cavernomas and both reported substantial improvement despite incomplete resection. Conclusions: Our case as well as literature review both show that surgical resection is beneficial in such cases and, even if subtotal, can result in substantial pain relief. This suggests intra-axial compression of the trigeminal spinal nucleus and tract as the possible cause of TGN pain in such cases.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".