The Acute Effect of Skin Preheating on Capsaicin‐Induced Central Sensitization in Humans
Bibliographic record
Abstract
INTRODUCTION: Topical capsaicin is commonly employed to experimentally induce central sensitization (CS) in humans. While previous studies have investigated the effect of skin preheating on the sensitizing effect of capsaicin, no studies have compared the synergistic effect of skin preheating on the magnitude of sensitization via topical capsaicin within the first 30 minutes of application. We tested the hypothesis that skin preheating potentiates the sensitizing effect of topical capsaicin by evoking a larger region of secondary hyperalgesia vs. topical capsaicin alone. METHODS: area of the dorsal forearm. The CAP + HEAT session also included a 10-minute preheating session. Regions of secondary hyperalgesia were assessed using mechanical brush allodynia testing, and skin temperature was assessed via infrared thermography. Outcomes were normalized to baseline and compared at 10, 20, and 30 minutes after cream application. RESULTS: The CAP + HEAT session led to a significantly larger area of secondary hyperalgesia compared to the CAP session as measured by brush allodynia (CON: 0 ± 0 cm; CAP: 2.08 ± 0.45 cm; CAP + HEAT: 3.70 ± 0.46 cm; P < 0.05) and skin temperature (CON: -2.92% ± 0.03%; CAP: -0.63% ± 0.09%; CAP + HEAT: 2.50% ± 0.11%; ( of baseline) P < 0.05). CONCLUSION: Preheating amplifies the sensitizing effect of topical capsaicin within 30 minutes of application. The heat-capsaicin technique may be employed to assess differing magnitudes of CS induction and enables future studies investigating the development and progression of CS in humans.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".