Normative data for the segmental acquisition of contact heat evoked potentials in cervical dermatomes
Bibliographic record
Abstract
Contact heat evoked potentials (CHEPs) represent a neurophysiological approach to assess conduction in the spinothalamic tract. The aim of this study was to establish normative values of CHEPs acquired from cervical dermatomes (C4, C6, C8) and examine the potential confounds of age, sex, and height. 101 (49 male) healthy subjects of three different age groups (18-40, 41-60, and 61-80 years) were recruited. Normal (NB, 35-52 °C) followed by increased (IB, 42-52 °C) baseline stimulation protocols were employed to record CHEPs. Multi-variate linear models were used to investigate the effect of age, sex, and height on the CHEPs parameters (i.e., N2 latency, N2P2 amplitude, rating of perceived intensity). Compared to NB, IB stimulation reduced latency jitter within subjects, yielding larger N2P2 amplitudes, and decreased inter-subject N2 latency variability. Age was associated with reduced N2P2 amplitude and prolonged N2 latency. After controlling for height, male subjects had significantly longer N2 latencies than females during IB stimulation. The study provides normative CHEPs data in a large cohort of healthy subjects from segmentally examined cervical dermatomes. Age and sex were identified as important factors contributing to N2 latency and N2P2 amplitude. The normative data will improve the diagnosis of spinal cord pathologies.
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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 | 0.002 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".