Discriminative ability of reflex receptive fields to distinguish patients with acute and chronic low back pain
Bibliographic record
Abstract
Low back pain has a life time prevalence of 70% to 85%. Approximately 10% to 20% of all patients experience recurrent episodes or develop chronic low back pain. Sociodemographic, clinical, and psychological characteristics explain the transition from acute to chronic low back pain only to a limited extent. Altered central pain processing may be a contributing mechanism. The measurement of reflex receptive fields (RRF) is a novel method to assess altered central pain processing. The RRF area denotes the area of the foot sole from which spinal nociceptive reflexes can be elicited. It was shown to be enlarged in patients with acute and chronic low back pain compared with pain-free individuals. The aim of the study was to explore the discriminative ability of the RRF to distinguish patients with acute and chronic low back pain with the hypothesis that enlarged RRF are associated with chronic low back pain. We included 214 patients with either acute or chronic low back pain and compared RRF between groups in both univariable and multivariable analyses adjusted for different sociodemographic and clinical characteristics possibly associated with the transition to chronic pain. We found a mean difference between patients with acute and chronic low back pain of -0.01 (95% confidence interval [CI], -0.06 to 0.04) in the crude, -0.02 (95% CI, -0.08 to 0.04) in the age and sex adjusted, and -0.02 (95% CI, -0.09 to 0.05) in the fully adjusted model. Our results suggest that the enlargement of RRF area may not be associated with the transition from acute to chronic low back pain.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".