Effects of Exercise Therapy on Endogenous Pain-relieving Peptides in Musculoskeletal Pain
Bibliographic record
Abstract
OBJECTIVE: To review the literature regarding the effects of exercise in patients with musculoskeletal pain on modifying: (1) the plasma or cerebral spinal fluid concentrations of pain-relieving peptides and (2) changing the cerebral activity of areas linked with pain processing and modulation systematically. METHODS: An extensive search of bibliographic databases including MEDLINE, EMBASE, EBM Reviews-Cochrane Central Register of Controlled Trials, ISI Web of Science, Scopus, PeDro, AMED, and CINAHL was made. Two independent investigators screened the titles of publications and completed quality assessment of the selected studies. RESULTS: The search of the literature resulted in a total of 1819 published studies. Of these only 1 study of low methodological quality was considered to be relevant. The agreement between reviewers to select the articles was κ=1. The agreement for the methodological quality evaluation was κ=0.9. DISCUSSION: Given the small number of studies identified and the low quality of research, no firm conclusions could be reached about the impact of therapeutic exercise on modifying concentrations of pain-relieving peptides or its effect on changing the cerebral activity of areas linked with pain processing in patients with musculoskeletal pain. There is a clear need for well-designed trials examining exercise therapy interventions and their effect on both pain-relieving peptides and cerebral activity in patients with musculoskeletal 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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".