Association of Trauma, Posttraumatic Stress Disorder, and Experimental Pain Response in Healthy Young Women
Bibliographic record
Abstract
BACKGROUND: Evidence of pain alterations in trauma-exposed individuals has been found. The presence of posttraumatic stress disorder (PTSD) may be explaining these alterations, as some of the psychological characteristics of PTSD are hypothesized to increase pain response. OBJECTIVES: To examine differences in pain response and in certain psychological variables between trauma-exposed women (TEW) with PTSD, TEW without PTSD, and non-trauma-exposed women (NTEW) and to explore the role of these psychological variables in the differences in pain response between the groups. METHODS: A total of 122 female students completed a cold pressor task (42 TEW with PTSD, 40 TEW without PTSD, and 40 NTEW). Anxiety sensitivity, experiential avoidance, trait and state dissociation, depressive symptoms, state anxiety, catastrophizing, and arousal were assessed. RESULTS: TEW with PTSD reported significantly higher pain unpleasantness than NTEW, but not more than that of TEW without PTSD. They also presented higher trait dissociation, state anxiety, depressive symptoms, and skin conductance than the other 2 groups and higher anxiety sensitivity than TEW without PTSD. TEW without PTSD reported more pain unpleasantness than NTEW, but they recovered faster from pain. However, these differences were not explained by any psychological variable. CONCLUSIONS: The results suggest that although trauma-exposed individuals are not more sensitive to painful stimulation, they evaluate pain in a more negative way. Exposure to trauma itself, but not to PTSD, may explain the differences found in pain unpleasantness.
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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 | 0.001 |
| 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".