Pain-related fear predicts reduced spinal motion following experimental back injury
Bibliographic record
Abstract
The current study examined the prospective relationship between pain-related fear and altered motor behavior, as well as perceived interference, among 51 healthy participants following induction of delayed-onset muscle soreness (DOMS) to the trunk extensor muscles. Healthy participants without history of back pain completed standardized reaches to high and low targets at self-paced and rapid speeds before and after induction of acute low back pain using a DOMS paradigm. Pain-related fear was assessed prior to DOMS induction. Three-dimensional joint motions of the thoracic spine, lumbar spine, and hip were recorded using an electromagnetic tracking device. DOMS-induced differences between high- and low-fear participants were observed for lumbar spine flexion, but not for thoracic or hip flexion. Pain-related fear scores were not predictive of lumbar flexion during baseline, but predicted reduced lumbar flexion during self- and fast-paced trials to low target locations once DOMS was induced. Pain-related fear was likewise predictive of perceived interference in life activities following DOMS induction. The findings suggest that initially pain-free individuals with high pain-related fear adopt avoidant spinal strategies during common reaching movements shortly after injury is sustained, which may comprise a risk factor for future pain and disability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".