Individual variability and sex differences in conditioned pain modulation and the impact of resilience, and conditioning stimulus pain unpleasantness and salience
Bibliographic record
Abstract
Distinct pain experiences are shaped both by personal attributes and characteristics of noxious stimuli. An Individual's capacity for endogenous pain inhibition (reflected by conditioned pain modulation [CPM]), their resilience, and the pain unpleasantness and salience of painful stimuli can impact their pain perception. Here, we aimed to determine how individual variability in CPM relates to sex and resilience as personal attributes, and pain unpleasantness and salience of the CPM conditioning stimulus (CS). We evaluated CPM in 106 healthy participants (51 female and 55 male) based on the change in test stimulus pain applied concurrently with a painful CS, both delivered by painful heat. The CS reduced test stimulus pain in only half of the participants (CPM subgroup), but did not do so for the other half (no-CPM subgroup), many who exhibited pain facilitation. A regression model explained CPM effects after accounting for sex, resilience, CS pain unpleasantness and salience. In the CPM subgroup regression model, the CPM effect was positively related to CS pain unpleasantness, while the CPM effect was not related to any variable in the no-CPM subgroup model. Correlation analyses revealed that the CPM effect was anticorrelated with resilience in males with no-CPM. The CPM effect was correlated with CS pain unpleasantness in males with CPM and in females with no-CPM. The CPM effect and CS salience were correlated in the whole group more strongly than in the subgroups. These data reveal that the complexity of contributors to CPM variability include both personal attributes and attributes of the CS.
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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) | 0.003 | 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".