Vestibular paroxysmia: Long-term clinical outcome after treatment
Bibliographic record
Abstract
Objective: To study the long-term treatment outcome of vestibular paroxysmia (VP). Study design: Retrospective study. Setting: Tertiary referral hospital. Methods: We analyzed records of 29 consecutive patients who were diagnosed with VP and who were treated with VP-specific anticonvulsants for at least 3 months. Patients were followed for a minimum of 6 months. We recorded and assessed starting and target dosage of medications, time to achieve adequate therapeutic response, adverse effects, and the rates of short-term and long-term remission without medication. Results: All 29 patients were started on oxcarbazepine as first-line treatment, and 93.1% and 100% of patients reported good-to-excellent therapeutic response within 2 and 4 weeks, respectively. Three patients switched to other anticonvulsants at 3 months. At long-term follow-up (8-56 months), most (84.6%) oxcarbazepine-treated patients maintained good therapeutic response at doses between 300 and 600 mg/day. Eleven (37.9%) patients experienced complete remission without medication for more than 1 month, of which six (20.7%) had long-term remission off medication for more than 12 months. Nineteen (65.5%) patients had neurovascular compression (NVC) of vestibulocochlear nerve on MRI, but its presence or absence did not predict treatment response or remission. Conclusion: Low-dose oxcarbazepine monotherapy for VP is effective over the long term and is generally well-tolerated. About 20% of patients with VP in our study had long-term remission off medication.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".