Differentiating trait pain from state pain: a window into brain mechanisms underlying how we experience and cope with pain
Bibliographic record
Abstract
Across various biological and psychological attributes, individuals have a set point around which they can fluctuate transiently into various states. However, if one remains in a different state other than their set point for a considerable period (eg, induced by a disease), this different state can be considered to be a new set point that also has associated surrounding states. This concept is instructive for understanding chronic pain, where an individual's set point may maladaptively shift such that they become stuck at a new set point of pain (trait pain), from which pain can fluctuate on different timescales (ie, pain states). Here, we discuss the importance of considering trait and state pains in neuroimaging studies of brain structure and function to gain an understanding of not only an individual's current pain state but also more broadly to their trait pain, which may be more reflective of their general condition.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".