Unraveling the pain trajectory in chronic low back pain patients during a physical exercise training program
Bibliographic record
Abstract
OBJECTIVES: Physical exercise can transiently decrease pain intensity within a single session and improve physical capacities while reducing pain over a training program. However, the pain trajectory throughout a concurrent physical training program remains unknown. This study aimed to model the pain trajectory during a training program including both aerobic and resistance exercises, considering both acute (within-session) and chronic (across-program) effects of physical exercise. DESIGN: Prospective observational study. METHODS: Participants completed a 14-week training program (42 sessions; n = 28) or were assigned to a waiting list (n = 29). In the exercise group, low back pain intensity was measured before and after each training session. Pain intensity in the past week was measured before and after the 14-week period in both groups. The pain trajectory was modeled using linear mixed-effects with a quadratic term to capture potential non-linear pain reduction. RESULTS: Past week pain decreased only in the exercise group (exercise: 4.9 ± 0.3 vs 2.5 ± 0.3; control: 5.6 ± 0.3 vs 5.3 ± 0.3). The pain trajectory was characterized by a linear and a quadratic term (p's < 0.001), suggesting pain reduction is greater early in the training program. Pain intensity decreased after each training session (p < 0.001) with this effect remaining constant throughout the program (non-significant interactions, p's > 0.585). CONCLUSIONS: Pain decreases more markedly during the initial weeks of the program. Acute exercise-induced hypoalgesia persisted throughout the program, suggesting patients may use physical exercise to manage pain flare-ups even after several weeks of training.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".