Characterization of a novel capsaicin/heat ongoing pain model
Bibliographic record
Abstract
BACKGROUND: Human experimental pain models provide an important translational link between pre-clinical models and clinical pain. Using topical capsaicin and continuous heat application, the novel capsaicin/heat ongoing pain (CHOP) model induces long-lasting experimental pain of which the perceived intensity can be individually adjusted. METHODS: In the CHOP model, capsaicin or control cream is applied to a 10 × 10 cm skin area and a heating pad is applied over the area after cream removal. Two experiments in healthy participants were performed for model characterization. In Experiment 1, a constant temperature was applied for 60 min; in Experiment 2, temperature was adjusted to maintain a constant perceived intensity for 60 min. RESULTS: Experiment 1: across participants, constant temperature induced initial habituation followed by an increase in sensation back to baseline. Cluster analysis revealed that half the participants sensitized to the constant temperature, while the other half did not. The degree of sensitization was related to the baseline pain unpleasantness, relative to pain intensity. Experiment 2: constant perceived intensity was achieved in the painful and a non-painful control condition. The two conditions did not differ regarding possibly confounding variables, including blood pressure, heart rate, inflammation or physiological stress as measured by surrogate markers. Secondary allodynia and hyperalgesia were reported more following painful compared to control stimulation. Sensitizers as determined in Experiment 1 were also more pain sensitive in Experiment 2. CONCLUSION: The CHOP model reproduces some aspects of clinical pain, such as longer duration, sensitization, secondary allodynia and hyperalgesia. SIGNIFICANCE: Here we demonstrate a novel pain model that can be applied for up to an hour without tissue damage. The CHOP model allows for investigation of primary and secondary hyperalgesia as well as top-down influences on sensitization, thereby providing an experimental model that can be used to assess clinically-oriented questions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".