Trauma, Pain, and Psychological Distress
Bibliographic record
Abstract
Abstract. Posttraumatic stress disorder (PTSD) and chronic musculoskeletal pain (CMP) are highly prevalent ( Breslau, 2002 ) and comorbid disorders ( Otis, Keane, & Kerns, 2003 ). The shared vulnerability model explains this overlap in part through a common attentional bias toward threat ( Asmundson, Coons, Taylor, & Katz, 2002 ). The current study made use of the acoustic startle to assess cognitive bias to threat in participants (n = 106; 64% women) who reported experiencing a motor vehicle accident (MVA). Participants were divided into five groups based on their diagnoses: PTSD, CMP, both PTSD and CMP, any general (i.e., non-PTSD) anxiety disorder with no CMP, and a no-disorder Control group. Self-report measures were used to assess psychological symptoms, trauma response, and pain-related factors. Word stimuli (i.e., trauma, sensory pain, health, pleasant, neutral) were presented visually prior to onset of the acoustic startle probe to assess for diagnosis-congruent attentional biases (e.g., persons with PTSD respond differently to trauma words). Relative to the general anxiety and control group, persons with PTSD or chronic pain demonstrated delayed startle peak and greater startle intensity across all word stimuli types; the results suggest there may be psychophysiologically measurable differences associated with PTSD and pain. The startle probe paradigm remains relatively nascent for such research, but has potential utility for assessment and treatment monitoring. Comprehensive results, discussion, and implications are analyzed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".