Dermatomal Somatosensory Evoked Potentials and Electrical Perception Thresholds During Recovery From Cervical Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Dermatomal somatosensory evoked potentials (dSSEPs) not only provide a neurophysiological readout comparable with conventional SSEPs but also provide an opportunity to track changes in sensory function corresponding to individual dermatomes (ie, a single spinal segment) above, at, and below the level of spinal cord injury (SCI). OBJECTIVES: This study aimed to determine the reliability and responsiveness of dSSEPs and electrical perception thresholds (EPTs) to monitor changes in sensory function after cervical SCI. METHODS: Initial and follow-up dSSEPs and EPTs were recorded from cervical dermatomes (C4-C8) of patients with traumatic tetraplegia (C3-C8; ASIA Impairment Scale A-D) during recovery after SCI (n = 18). RESULTS: Follow-up examination of 74 initial dSSEPs unaffected by SCI (n = 18) revealed no significant change in latency (Delta = 0.0 +/- 1.4 ms; P = .9) or EPT sensitivity (Delta = 0.1 +/- 0.8 mA; P = .3). In 41 dSSEPs initially delayed after SCI (n = 14), latencies significantly decreased on follow-up examination (Delta = -3.1 +/- 2.9 ms; P < .01) without a corresponding increase in sensitivity of the EPT (Delta = 0.2 +/- 3.4 mA; P = .7). dSSEPs that were not measurable initially were subsequently recorded in 11 dermatomes (n = 5) on follow-up examination. This conversion of abolished-to-recordable dSSEPs was often preceded by the perception of an initial EPT and associated with a concomitant recovery of EPT at follow-up. CONCLUSION: SSEPs and EPT can be reliably recorded to monitor changes in sensory function for each individual spinal segment after cervical SCI. dSSEPs may be potentially useful to monitor the safety of a therapeutic drug or cell transplant in early-phase (I/II) clinical trials as well as document the potential efficacy of interventions where the standard neurological assessment might not detect subtle therapeutic effects.
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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 | 0.000 | 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".