Contact Heat Evoked Potentials Are Responsive to Peripheral Sensitization: Requisite Stimulation Parameters
Bibliographic record
Abstract
The sensitizing effect of capsaicin has been previously characterized using laser and contact heat evoked potentials (LEPs and CHEPs) by stimulating in the primary area of hyperalgesia. Interestingly, only CHEPs reveal changes consistent with notion of peripheral sensitization (i.e., reduced latencies). The aim of this study was to investigate contact heat stimulation parameters necessary to detect peripheral sensitization related to the topical application of capsaicin, and therefore significantly improve the current method of measuring peripheral sensitization via CHEPs. Rapid contact heat stimulation (70°C/s) was applied from three different baseline temperatures (35, 38.5, and 42°C) to a 52°C peak temperature, before and after the topical application of capsaicin on the hand dorsum. Increased pain ratings in the primary area of hyperalgesia were accompanied by reduced N2 latency. Changes in N2 latency were, however, only significant following stimulation from 35 and 38.5°C baseline temperatures. These findings suggest that earlier recruitment of capsaicin-sensitized afferents occurs between 35 and 42°C, as stimulations from 42°C baseline were unchanged by capsaicin. This is in line with reduced thresholds of type II A-delta mechanoheat (AMH) nociceptors following sensitization. Conventional CHEP stimulation, with a baseline temperature below 42°C, is well suited to objectively detect evidence of peripheral sensitization.
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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".