Post-concussion abnormalities in the m60 somatosensory evoked field recorded by magnetoencephalography
Bibliographic record
Abstract
Objective We sought to determine whether magnetoencephalography (MEG) might reliably document the presence of neurophysiologic dysfunction after acute concussion. Based on a previous evoked potential study,1we analysed somatosensory evoked fields (SEFs) elicited by stimulation of the median nerve at the wrist. Design Case-control study. Setting Academic tertiary care centre. Participants Five patients with acute concussion (<10 days post-injury); mean age 32±18 yrs, 3 women. 23 age/sex matched controls with no history of concussion. Assessment of risk factor Independent variable, concussion. Outcome measures Dependent variables, bilateral peak latencies of M20, M30, and M60 SEFs. Main results No differences were found between patients (24 recorded values) and controls (46 recorded values) in M20 (21.7±1.6 ms vs. 22.3±1.5 ms) or M30 (35.4±3.8 ms vs. 33.5±3.3 ms) latencies. M60 latencies were significantly delayed in patients compared to controls (90.4±21.5 ms vs. 64.0±8.6 ms; Mann Whitney U-test, U=186.5, p=0.0001). All symptomatic patients had an abnormally delayed M60 value>2 SDs from the control mean on at least one side. Follow-up patient recordings showed gradual decreases in abnormal M60 latencies over months; weekly MEG and ImPACT testing in one patient showed normalisation of verbal memory at week 2, visual memory and symptom scores at week 5 and M60 latencies at week 10. Conclusions The M60 SEF represents the first intracortical stage of neuronal processing of external somatosensory input to the brain. In this preliminary study we found significant differences in M60 latencies between subjects with acute concussion and controls, as well as within subject changes during recovery in the weeks after concussion. Competing interests None.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".