Hypertrophic Interstitial Neuropathy of the Trigeminal Nerve: Case Report and Literature Review
Bibliographic record
Abstract
BACKGROUND AND IMPORTANCE: Hypertrophic interstitial neuropathy (HIN) is an uncommon, non-neoplastic lesion typically affecting peripheral nerves. Cranial nerve (CN) involvement is exceedingly rare. We present a case of isolated trigeminal nerve HIN manifesting with V3 distribution neuralgia. CLINICAL PRESENTATION: A 50-yr-old male presented with left sided trigeminal neuralgia refractory to medical management. The patient underwent retromastoid craniectomy for possible microvascular decompression. Intra-operatively, the trigeminal nerve appeared to be focally enlarged with a sausage-like configuration. We selectively resected 1 fascicle which was predominantly involved. Histopathological examination revealed onion bulb formations composed of Schwann cells around centrally placed axons. A diagnosis of HIN was made. Postoperatively, the patient experienced complete resolution of symptoms. CONCLUSION: This is the third case of isolated trigeminal nerve HIN in the literature. We performed a selective resection in a patient presenting with trigeminal neuralgia, resulting in complete resolution of symptoms. It is reported here with intraoperative microscope images, along with a review and analysis of this topic as it related to CN.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".