Constant Face Pain in Typical Trigeminal Neuralgia and Response to Gamma Knife Radiosurgery
Bibliographic record
Abstract
BACKGROUND/AIMS: Constant pain, especially if prominent, is sometimes considered incompatible with a diagnosis of typical idiopathic trigeminal neuralgia. This study aims to clarify the frequency of patient-reported constant pain in patients with medically intractable, typical, idiopathic trigeminal neuralgia as diagnosed with standard clinical parameters and confirmed by the response to a modified McGill questionnaire, a 'hold-still' test that eliminated triggers and the response to Gamma Knife radiosurgery. METHOD: Forty consecutive patients with typical trigeminal neuralgia were given questionnaires prior to Gamma Knife radiosurgery. Those with constant pain were further tested by being advised to hold completely still for up to 3 min. Final pain relief was evaluated after Gamma Knife radiosurgery. RESULTS: Twenty of forty patients indicated on a questionnaire that they had constant face pain. Pain decreased on the 'hold-still' test on all 12 patients who were tested. Following Gamma Knife radiosurgery, there was no significant difference in pain relief in those without or with constant pain. CONCLUSION: Patients with typical idiopathic trigeminal neuralgia frequently report that 50% or more of their pain is constant. This constant pain is markedly decreased if the patient holds completely still for a few minutes and does not affect the outcome of Gamma Knife radiosurgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".