Recurrent pain is associated with decreased selective attention in a population-based sample
Bibliographic record
Abstract
Studies which have examined the impact of pain on cognitive functioning in the general population are scarce. In the present study we assessed the predictive value of recurrent pain on cognitive functioning in a population-based study (N=1400). Furthermore, we investigated the effect of pain on cognitive functioning in individuals with specific pain complaints (i.e. back pain, gastric pain, muscle pain and headache). Cognitive functioning was assessed using the Stroop Color-Word Interference test (Stroop interference), the Letter-Digit-Substitution test (LDST) and the Visual Verbal learning Task (VVLT). Pain was measured with the COOP/WONCA pain scale (Dartmouth Primary Care Cooperative Information Project/World Organization of National Colleges, Academies, and Academic Associations of General Practice /Family Physicians). We controlled for the effects of age, sex, level of education and depressive symptoms. It was demonstrated that pain had a negative impact on the performance on the Stroop interference but not on the VVLT and the LDST. This indicates that subjects who reported extreme pain had more problems with selective attention and were more easily distracted. Effects were in general larger in the specific pain groups when compared to the associations found in the total group. Implications of these findings are discussed. The experience of recurrent pain has a negative influence on selective attention in a healthy population.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".