The Emotional Brain as a Predictor and Amplifier of Chronic Pain
Bibliographic record
Abstract
Human neuroimaging studies and complementary animal experiments now identify the gross elements of the brain involved in the chronification of pain. We briefly review these advances in relation to somatic and orofacial persistent pain conditions. First, we emphasize the importance of reverse translational research for understanding chronic pain-that is, the power of deriving hypotheses directly from human brain imaging of clinical conditions that can be invasively and mechanistically studied in animal models. We then review recent findings demonstrating the importance of the emotional brain (i.e., the corticolimbic system) in the modulation of acute pain and in the prediction and amplification of chronic pain, contrasting this evidence with recent findings regarding the role of central sensitization in pain chronification, especially for orofacial pain. We next elaborate on the corticolimbic circuitry and underlying mechanisms that determine the transition to chronic pain. Given this knowledge, we advance a new mechanistic definition of chronic pain and discuss the clinical implications of this new definition as well as novel therapeutic potentials suggested by these advances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".