Test–Retest Reliability of Contact Heat-Evoked Potentials From Cervical Dermatomes
Bibliographic record
Abstract
The purpose of this study was to investigate the test-retest reliability of contact heat-evoked potentials (CHEPs) in neurologically healthy subjects from cervical dermatomes (C4-C8). Seventeen individuals underwent test-retest examination of cervical CHEPs. Peak latencies and peak-to-peak amplitude of N2-P2 and ratings of perceived intensity were analyzed using test-retest reliability statistics (intraclass correlation coefficients [ICCs] and Bland-Altman confidence parameters). For comparison, a group of similar age and gender was also examined with dermatomal somatosensory-evoked potentials (dSSEPs, n = 17). The ICC values for CHEP latency and amplitude parameters were significant (P < 0.05) and corresponded to at least "fair" reliability, while peak-to-peak amplitude demonstrated "substantial" (≥0.81) reliability for all dermatomes. The coefficients of repeatability (i.e., 2SD of the difference between examinations) confirm that CHEPs and dSSEPs are reliable according to measures of latency. Superior peak-to-peak amplitude test-retest reliability was found for CHEPs. In conclusion, the test-retest reliability of dSSEP and CHEP parameters supports the fact that these outcomes can be used to objectively track changes in spinal conduction in the dorsal column and spinothalamic tract, respectively. The reliable acquisition of CHEPs may depend on the intensity of the sensation reported by the subject for a given area of skin stimulated.
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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 | 0.006 | 0.024 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".