Sensory phenotypes in complex regional pain syndrome and chronic low back pain—indication of common underlying pathomechanisms
Bibliographic record
Abstract
Introduction: First-line pain treatment is unsatisfactory in more than 50% of chronic pain patients, likely because of the heterogeneity of mechanisms underlying pain chronification. Objectives: This cross-sectional study aimed to better understand pathomechanisms across different chronic pain cohorts, regardless of their diagnoses, by identifying distinct sensory phenotypes through a cluster analysis. Methods: We recruited 81 chronic pain patients and 63 age-matched and sex-matched healthy controls (HC). Two distinct chronic pain cohorts were recruited, ie, complex regional pain syndrome (N = 20) and low back pain (N = 61). Quantitative sensory testing (QST) was performed in the most painful body area to investigate somatosensory changes related to clinical pain. Furthermore, QST was conducted in a pain-free area to identify remote sensory alterations, indicating more widespread changes in somatosensory processing. Results: Two clusters were identified based on the QST measures in the painful area, which did not represent the 2 distinct pain diagnoses but contained patients from both cohorts. Cluster 1 showed increased pain sensitivities in the painful and control area, indicating central sensitization as a potential pathomechanism. Cluster 2 showed a similar sensory profile as HC in both tested areas. Hence, either QST was not sensitive enough and more objective measures are needed to detect sensitization within the nociceptive neuraxis or cluster 2 may not have pain primarily because of sensitization, but other factors such as psychosocial ones are involved. Conclusion: These findings support the notion of shared pathomechanisms irrespective of the pain diagnosis. Conversely, different mechanisms might contribute to the pain of patients with the same diagnosis.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".