Can we characterize A-P/IAP behavioural phenotypes in people with chronic pain?
Bibliographic record
Abstract
-type) or faster (A-type) during experimental pain. These behavioural phenotypes were not previously studied in chronic pain populations to avoid using experimental pain in a chronic pain context. Since pain rumination (PR) may serve as a supplement to IAP without needing noxious stimuli, we attempted to delineate A-P/IAP behavioural phenotypes in people with chronic pain and determined if PR can supplement IAP. Behavioural data acquired in 43 healthy controls (HCs) and 43 age-/sex-matched people with chronic pain associated with ankylosing spondylitis (AS) was retrospectively analyzed. A-P behavioural phenotypes were based on RT differences between pain and no-pain trials of a numeric interference task. IAP was quantified based on scores representing reported attention towards or mind-wandering away from experimental pain. PR was quantified using the pain catastrophizing scale, rumination subscale. The variability in RT was higher during no-pain trials in the AS group than HCs but was not significantly different in pain trials. There were no group differences in task RTs in no-pain and pain trials, IAP or PR scores. IAP and PR scores were marginally significantly positively correlated in the AS group. RT differences and variability were not significantly correlated with IAP or PR scores. Thus, we propose that experimental pain in the A-P/IAP protocols can confound testing in chronic pain populations, but that PR could be a supplement to IAP to quantify attention to pain.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".