Effect of Static and Dynamic Heat Pain Stimulus Profiles on the Temporal Dynamics and Interdependence of Pain Qualities, Intensity, and Affect
Bibliographic record
Abstract
Acute and chronic pains are characterized by a particular constellation of pain qualities, such as burning, aching, stinging, or sharp feelings. However, the temporal pattern of specific pain qualities and their relationship with pain and affect is not well understood. In addition, little is known about how the temperature time course of the stimulus impacts the temporal dynamics of pain qualities and the relationship between pain qualities. Therefore we applied two types of stimuli to the feet of 16 healthy subjects, each calibrated to evoke a similar pain magnitude (50/100): static stimulus held at constant intensity and dynamic stimulus increased in intensity in small steps. Stimulus runs consisted of three 30-s stimuli (either static or dynamic) with an interstimulus interval of 60 s. Continuous on-line ratings of pain, burning, sharp, stinging, cutting, and annoyance were obtained in separate runs, and the evoked responses were characterized by within-stimulus adaptation (early: 0- to 15-s peak vs. late: 25- to 40-s peak) and by their temporal properties (time to onset, peak, and end). The temporal profile of the burning sensation was similar to the pain and annoyance evoked by the static and dynamic stimuli. However, the sharp, stinging and cutting sensations attenuated in response to the static stimuli (P<0.01) but intensified along with pain and affect in response to the dynamic stimuli (P<0.05), whereas there was no attenuation in the evoked profiles of pain (P=0.61), annoyance (P=0.27), or burning quality (P=0.27). These data demonstrate that specific pain qualities with known differences in underlying mechanisms have distinct temporal dynamics that depend on the stimulus intensity dynamics.
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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 | 0.002 |
| 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".