Improved acquisition of contact heat evoked potentials with increased heating ramp
Bibliographic record
Abstract
Contact heat evoked potentials (CHEPs) represent an objective and non-invasive measure to investigate the integrity of the nociceptive neuraxis. The clinical value of CHEPs is mostly reflected in improved diagnosis of peripheral neuropathies and spinal lesions. One of the limitations of conventional contact heat stimulation is the relatively slow heating ramp (70 °C/s). This is thought to create a problem of desynchronized evoked responses in the brain, particularly after stimulation in the feet. Recent technological advancements allow for an increased heating ramp of contact heat stimulation, however, to what extent these improve the acquisition of evoked potentials is still unknown. In the current study, 30 healthy subjects were stimulated with contact heat at the hand and foot with four different heating ramps (i.e., 150 °C/s, 200 °C/s, 250 °C/s, and 300 °C/s) to a peak temperature of 60 °C. We examined changes in amplitude, latency, and signal-to-noise ratio (SNR) of the vertex (N2-P2) waveforms. Faster heating ramps decreased CHEP latency for hand and foot stimulation (hand: F = 18.41, p < 0.001; foot: F = 4.19, p = 0.009). Following stimulation of the foot only, faster heating ramps increased SNR (F = 3.32, p = 0.024) and N2 amplitude (F = 4.38, p = 0.007). Our findings suggest that clinical applications of CHEPs should consider adopting faster heating ramps up to 250 °C/s. The improved acquisition of CHEPs might consequently reduce false negative results in clinical cohorts. From a physiological perspective, our results demonstrate the importance of peripherally synchronizing afferents recruitment to satisfactorily acquire CHEPs.
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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 | 0.001 |
| 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) | 0.002 | 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".