Effects of High Stimulus Frequencies on SEPs of Patients with Neuro-Behcet's Disease
Bibliographic record
Abstract
BACKGROUND: Behcet's disease is a multisystemic vascular inflammatory disorder of unknownorigin. It is relatively rare and central nervous system involvement is seen in 5% of affected individuals. Somatosensory evoked potentials (SEPs) can provide information that shows the presence of clinically unsuspected lesions in the central nervous system of these patients. However, the effects of changing the stimulus frequencies on latencies of SEP potentials and central conduction time (CCT) in patients with neuro-Behcet's disease (NB) have not been studied yet. In this study, our aim was to reveal these effects to investigate whether the change of stimulus frequencies could be of convenient use in obtaining more accurate CCT estimations in SEP studies of these patients. METHODS: We performed median nerve SEPs of 14 patients with NB and 15 healthy volunteers. We changed the stimulus frequency: 2 Hz, 4Hz, 6Hz and 9Hz in successive recordings and statistically compared the changes on SEP potentials and peak and onset CCT in the neuro-Behcet (NB) group and the normal group. RESULTS: Our results indicated that the onset CCT values of the NB group were higher than the normal group at 4Hz and 9Hz stimulations. However, the comparison of peak CCT in the NB group and the normal group did not show any statistically meaningful differences at all stimulation frequencies. CONCLUSION: Onset CCT has not been measured before in former SEP studies of patients with NB. We highly recommend measuring onset CCT at higher stimulation frequencies in order to reveal central conduction time pathologies in these patients.
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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 | 0.002 |
| 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) | 0.001 | 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".