Association between Trigeminal Neuralgia and Multiple Sclerosis: Role of Magnetic Resonance Imaging
Bibliographic record
Abstract
Background: Trigeminal neuralgia (TN) is a severe neuropathic unilateral facial pain affecting about 30% percent of the world population. Neuropathic pains are considered to be associated with multiple sclerosis (MS).Multiple sclerosis is a chronic inflammatory condition causing demyelination and degeneration of axons in central nervous system. Objective: The objective of the study is to determine role of Magnetic Resonance Imaging to find association between trigeminal neuralgia and multiple sclerosis. Methods: The prospective cohort study was conducted for six months in Radiology Department of Hayatabad Medical Complex, Peshawar from September 2020 to February 2021. Initially 250 patients were screened for multiple sclerosis. The study recruited a total of 35 patients of MS visited neuroradiology department, out of which 26 patients were enrolled in the study. The participants with age of 18 years and onward of both genders with definitive symptoms of TN with MS that is having unilateral TN pain (that is sharp shooting electric pulse like) lasting for up-to 2minutes precipitated with an environmental stimulus were included in the study. The patients (n=6) with bilateral MS with TN and cognitive disturbances (n=3) were excluded from the study. Results: The study recruited a total of 26 participants with MS related TN. The clinical examination didn’t show any difference between the three groups with the p-value less than 0.001. Age at the onset of MS was younger in patients with MS related sensory disturbances compared to other two groups, with p-value less than 0.05. The frequency of the affected side was different in all three groups with the p-value less than 0.05 as tested by Fischer exact test. Trigeminal reflex tests done for different components such as R1 and SP1 showed longer latency periods for the affected side after stimulation and unaffected side after stimulation with the mean of 14.2± 4.4 and 15.3±3.2, 16.3±4.2 and 17.4±5.2ms and p-value less than 0.001 as shown by Wilcoxon test. Conclusion: The study showed significant association between trigeminal neuralgia and multiple sclerosis with the greater efficacy of using MRI as imaging technique to find this association. Keywords: Multiple sclerosis, Magnetic Resonance Imaging, Trigeminal neuralgia
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".