Strategic and Automatic Threat Processing in Chronic Musculoskeletal Pain: A Startle Probe Investigation
Bibliographic record
Abstract
Attentional bias research with chronic pain samples has yielded conflicting results. In the present investigation the startle paradigm was used to test the postulate that fear-based mechanisms play an important role in attentional biases for pain-related threat in chronic pain. Participants, including 31 individuals with chronic musculoskeletal pain and 20 healthy controls, completed a startle task designed to measure attention to different types of words (neutral vs sensory pain vs affective pain vs health catastrophe) presented at different levels of cognitive processing (strategic vs automatic). Measures of fear-based individual difference variables, including anxiety sensitivity and fear of pain, were also completed. Startle amplitudes and latencies to acoustic startle probes that followed word presentations were recorded. Data were analyzed with repeated measures ANOVAs and correlational analysis. Significant between-group differences were found indicating that, relative to chronic pain participants, healthy controls had higher startle amplitude index scores for health catastrophe words. There was also a trend among patients with chronic pain for greater startle amplitude index scores for strategic presentations of sensory pain words. In the automatic condition, all participants demonstrated a lower startle latency index for sensory words relative to both affect and health catastrophe words, suggesting participants had more difficulty disengaging from affect and health catastrophe words or were more avoidant of sensory words. Correlational analyses indicated that startle response indices for words related to health catastrophe became more pronounced for chronic pain patients as anxiety sensitivity and fear of pain increased. Implications and directions for future research are discussed.
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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 | 0.003 |
| 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) | 0.001 | 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".