The Role of Pain Catastrophizing in Experimental Pain Perception
Bibliographic record
Abstract
BACKGROUND: Pain is a subjective experience influenced by multiple factors, and tremendous variety within individuals is present. To evaluate emotional state of pain, catastrophizing score can be used. This study investigated pain catastrophizing ratings in association with experimental pain perception. METHOD: Experimental pain was induced using thermal heat and cold stimulation of skin, mechanical stimulation of muscle and bone, and thermal, mechanical, and electrical stimulation of the gastrointestinal tract in healthy participants (N = 41). Prior to experimental sessions, a pain catastrophizing questionnaire was filled out by each participant. RESULTS: Based on the median catastophizing score, participants were divided into two groups: noncatastrophizers and low-catastrophizers. No significant difference was found between low-catastrophizers and noncatastrophizers in thermal heat stimulation of skin, mechanical stimulation of muscle and bone, and rectal electrical stimulation (All P > 0.05). Low-catastrophizers were more sensitive to visceral thermal stimulation (4.7%, P = 0.02) and visceral mechanical stimulation (29.7%, P = 0.03). For participants that completed the 120 seconds ice water stimulation, noncatastrophizers reported 13.8% less pain than low-catastrophizers (P = 0.02). A positive correlation between PCS score and pain perception on cold pressor test was found (r = 0.4, P = 0.02). By extrapolating data, further analysis of the total group was performed and no differences (both P > 0.05) were observed. CONCLUSION: Even small increments in pain catastrophizing score can influence pain perception to deep and tonic stimulations. Catatrophizing may partly explain the variability found in experimental pain studies.
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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) | 0.002 | 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".