Neuropathic Pain in Ankylosing Spondylitis: A Psychophysics and Brain Imaging Study
Bibliographic record
Abstract
OBJECTIVE: To determine whether there is a neuropathic component in ankylosing spondylitis (AS) back pain and to delineate gray matter brain abnormalities associated with AS. METHODS: Seventeen patients with back pain secondary to AS who were not receiving biologic agents and 17 age- and sex-matched healthy controls consented to participate in the study and were assessed using the painDETECT instrument (scores of ≤12 indicating low probability of neuropathic pain) and the McGill Pain Questionnaire. Mechanical and thermal thresholds were determined in all subjects, and brain gray matter was assessed by 3T magnetic resonance imaging. RESULTS: Eleven of the 17 AS patients had painDETECT scores of >12. The patients had decreased mechanical and cold sensitivity on the dorsum of their feet but did not have altered pain thresholds. Compared to controls, the AS patients exhibited cortical thinning in the primary somatosensory, insular, anterior cingulate, and anterior mid-cingulate cortices and the supplemental motor area, and increased gray matter volume in the thalamus and putamen. Scores on the painDETECT in AS patients were correlated with decreased gray matter in the primary somatosensory cortex and with increased gray matter in the motor cortex, anterior cingulate cortex, prefrontal cortex, thalamus, and striatum. CONCLUSION: The present findings indicate that neuropathic pain occurs in AS. Furthermore, abnormal brain gray matter and neural correlates of neuropathic pain are concordant with the clinical picture of AS, which includes sensorimotor and mood deficits as well as neuropathic pain symptoms. These results suggest that back pain in AS is a mixed pain condition that includes a neuropathic pain component.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.002 | 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".