Unpredictable pain timings lead to greater pain when people are highly intolerant of uncertainty
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Many psychological factors are known to influence pain perception. Among them, intolerance of uncertainty (IU) may play a key modulating role in situations where uncertainty prevails, especially uncertainty regarding the timing of painful events. The objective of this study was to explore the impact of individual differences in IU on pain perception during predictable and unpredictable stimulation timings. We hypothesized that people with high IU, as opposed to those with low IU, would perceive more pain when the timing of painful stimulations cannot be predicted, as compared to when they can. METHODS: Twenty (20) healthy adults, aged between 18 and 35 years old, were recruited. Painful sensations were provoked using transcutaneous electrical stimulations of the right sural nerve. By measuring IU (Intolerance of Uncertainty Scale) and subjective pain (verbal numerical rating scale), it was possible to test the relationship between IU and pain perception, by simulating predictable and unpredictable painful experiences. This was done through cued shock interval (CSI) blocks, with either variable timing or fixed timings (long or short time frame). Self-administered questionnaires were also used to measure pain hypervigilance, pain catastrophizing, state anxiety, and trait anxiety. RESULTS: Pearson correlations confirmed the presence of an association (r=0.63) between IU and the change in pain intensity provoked by unpredictable stimulation timings. Importantly, this association was significant only for stimulations provided at long CSIs, indicating that higher IU scores predicted higher pain intensity scores when stimulation timings became unpredictable, and when the cued delay was long. No association was found between pain scores and other psychological variables. CONCLUSIONS: Our results show that IU moderately correlates to the change in pain intensity provoked by unpredictable stimulation timings. High IU scores were associated with a worsening of the subjective pain experience, especially during long delays in an unpredictable situation. These observations suggest that IU could be considered as a psychological variable that is able to influence pain perception in certain situations. IMPLICATIONS: Assessing and addressing IU could be an added value in pain-related therapy, especially in chronic pain.
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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 | 0.001 | 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".