Indication for spinal sensitization in chronic low back pain: mechanical hyperalgesia adjacent to but not within the most painful body area
Bibliographic record
Abstract
Abstract Introduction: In 85% of patients with chronic low back pain (CLBP), no specific pathoanatomical cause can be identified. Besides primary peripheral drivers within the lower back, spinal or supraspinal sensitization processes might contribute to the patients' pain. Objectives: The present study conceptualized the most painful area (MP) of patients with nonspecific CLBP as primarily affected area and assessed signs of peripheral, spinal, and supraspinal sensitization using quantitative sensory testing (QST) in MP, a pain-free area adjacent to MP (AD), and a remote, pain-free control area (CON). Methods: Fifty-nine patients with CLBP (51 years, SD = 16.6, 22 female patients) and 35 pain-free control participants individually matched for age, sex, and testing areas (49 years, SD = 17.5, 19 female participants) underwent a full QST protocol in MP and a reduced QST protocol assessing sensory gain in AD and CON. Quantitative sensory testing measures, except paradoxical heat sensations and dynamic mechanical allodynia (DMA), were Z-transformed to the matched control participants and tested for significance using Z-tests (α = 0.001). Paradoxical heat sensations and DMA occurrence were compared between cohorts using Fisher's exact tests (α = 0.05). The same analyses were performed with a high-pain and a low-pain CLBP subsample (50% quantile). Results: Patients showed cold and vibration hypoesthesia in MP (all Ps < 0.001) and mechanical hyperalgesia (P < 0.001) and more frequent DMA (P = 0.044) in AD. The results were mainly driven by the high-pain CLBP subsample. In CON, no sensory alterations were observed. Conclusion: Mechanical hyperalgesia and DMA adjacent to but not within MP, the supposedly primarily affected area, might reflect secondary hyperalgesia originating from spinal sensitization in patients with CLBP.
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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) | 0.003 | 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".