Data-driven dynamic profiles of tonic heat pain perception in pain-free volunteers are associated with differences in anandamide levels
Bibliographic record
Abstract
Our laboratory previously developed a method for assessing experimentally induced pain perception through a 2-min constant heat pain stimulation. However, the traditional analysis relying on group means struggles to interpret the considerable inter-individual variability due to the dynamic nature of the response. Recently, trajectory analysis techniques based on extended mixed models have emerged, providing insights into distinct response profiles. Notably, these methods have never been applied to pain paradigms before. Furthermore, various socio-demographic and neurobiological factors, including endocannabinoids, may account for these inter-individual differences. This study aims to apply the novel analysis to dynamic pain responses and investigate variations in response profiles concerning socio-demographic, psychological, and blood endocannabinoid concentrations. 346 pain-free participants were enrolled in a psychophysical test involving a continuous painful heat stimulation lasting for 2 min at a moderate intensity. Pain perception was continuously recorded using a computerized visual scale. Dynamic pain response analyses were conducted using the innovative extended mixed model approach. In contrast to the traditional group-mean analysis, the extended mixed model revealed three pain response trajectories. Trajectory 1 is characterized by a delay peak pain. Trajectory 2 is equivalent to the classic approach (peak pain follow by a constant and moderate increase of pain perception). Trajectory 3 is characterized by extreme responses (steep peak pain, decrease, and increase of pain perception), Furthermore, age and blood anandamide levels exhibited significant variations among these three trajectories. Using an innovative statistical approach, we found that a large proportion of our sample had a response significantly different from the average expected response. Endocannabinoid system seems to play a role in pain response profile.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".