Dot-probe evaluation of selective attentional processing of pain cues in patients with chronic headaches
Bibliographic record
Abstract
Evidence supporting the notion that patients with chronic pain are characterized by attentional biases for sensory and affect pain words, and that such biases are mediated by fear of pain, is mixed. The present investigation was an attempt to replicate and extend initial findings obtained with the dot-probe task. Thirty patients with chronic headache and 19 healthy controls were tested using a dot-probe task including affect pain, sensory pain, and neutral words. Individual difference variables, including fear of pain measures, were assessed and considered in analyses. Selective attention was denoted using the bias index, congruency index, and incongruency index. There were no significant between-group differences or interactions between group and word type observed for any of the indices of selective attention. Across groups there was evidence for a significant association between anxiety sensitivity and the bias index for sensory pain words, and between affective description of current pain and the incongruency index for affect pain words. These results do not provide convincing evidence that patients with chronic headache selectively attend to affect or sensory pain cues when compared to healthy controls. The significant cross-groups associations between anxiety sensitivity and current pain description and indices of selective attention are consistent with the notion that attentional biases may be influenced by fear propensity and current concerns. Implications of the findings and future research directions 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) | 0.002 | 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".