Cortical Relay Time for Long Latency Reflexes in Patients with Definite Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Long latency reflexes (LLR) include afferent sensory, efferent motor and central transcortical pathways. It is supposed that the cortical relay time (CRT) reflects the conduction of central transcortical loop of LLR. Recently, evidence related to the cortical involvement in multiple sclerosis (MS) has been reported in some studies. Our aim was to investigate the CRT alterations in patients with MS. METHODS: Upper extremity motor evoked potentials (MEP), somatosensory evoked potentials (SEP) and LLR were tested in 28 patients with MS and control subjects (n=22). The patients with MS were classified according to the clinical form (relapsing-remitting [R-R] and progressive groups). The MS patients with secondary progressive and primary progressive forms were considered as the "progressive" group. CRT for LLR was calculated by subtracting the peak latency of somatosensory evoked potentials (SEP) and that of motor evoked potentials (MEP) by transcranial magnetic stimulation from the onset latency of the second component of LLR (LLR2) (CRT = LLR2 - [MEP latency + N20 latency]) RESULTS: Cortical relay time was calculated as 7.4 +/- 0.9 ms in control subjects. Cortical relay time was prolonged in patients with MS (11.2 +/- 2.9 ms) (p<0.0001). The latencies of LLR, MEP and SEP were also prolonged in patients with MS. Cortical relay time was not correlated with disease severity and clinical form in contrast to other tests. CONCLUSIONS: Our findings suggested that CRT can be a valuable electrophysiological tool in patients with MS. Involvement of extracortical neural circuits between sensory and motor cortices or cortical involvement due to MS may cause these findings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".