Automatic and Strategic Processing of Threat Cues in Patients With Chronic Pain: A Modified Stroop Evaluation
Bibliographic record
Abstract
OBJECTIVE: The goal of this study was to clarify whether patients with chronic pain selectively attend to syndrome-specific (i.e., pain-related) information and, if so, to determine whether this occurs at the conscious (i.e., strategic) or unconscious (i.e., automatic) level. SETTING: This study was conducted at a tertiary care rehabilitation center. PATIENTS: Thirty-three patients with chronic back and/or neck pain and 33 healthy volunteers matched for age, sex, and education participated in this study. OUTCOME MEASURES: A computerized version of a modified Stroop color-naming task, with unmasked and masked conditions, was used to assess strategic and automatic information processing of words related to sensory pain, affect pain, physical threat, social threat, and neutral themes. RESULTS: A repeated-measures ANOVA indicated that patients with chronic pain but not healthy volunteers had delayed color-naming latencies to both sensory and affect pain words in the unmasked condition. Color-naming latency differences were not evident for other word types in the unmasked condition or for any word types in the masked condition. Correlational and regression analyses indicated that the delayed color-naming latencies to pain words in the unmasked condition observed for the chronic pain patients were, in part, associated with high pain-specific cognitive anxiety and interference and lower levels of anxiety sensitivity. CONCLUSIONS: Individuals with chronic pain selectively process pain-related cues at the strategic level but not at the automatic level. 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".