Stress-induced Physiologic Changes as a Basis for the Biopsychosocial Model of Chronic Musculoskeletal Pain
Bibliographic record
Abstract
OBJECTIVES: (1) To integrate the scientific literatures of the biopsychosocial model of chronic musculoskeletal pain and of stress-induced physiologic wound and muscle changes, and (2) to propose a clinical assessment and treatment model that incorporates this dual literature into the management of chronic musculoskeletal pain. METHODS: English language literature search from January 1990 to February 2008 using the MEDLINE and PsycINFO databases and the keywords "wound healing," "musculoskeletal injury," "skeletal muscle injury," "psychological," "social," "stress," "anxiety," and "pain." RESULTS: An illustrative case report is introduced. Review of the scientific literature revealed that psychologic stress is associated with slower or delayed wound healing in stressed older adults, restrained mice, socially isolated hamsters, adults with leg wounds, and surgical patients. One study showed that expressive writing positively affected the healing of a small skin puncture. Psychosocial stress affected aspects of muscle activity and spinal loading. Slaughter studies demonstrated that high preslaughter stress in pigs negatively affected postslaughter meat quality. A clinical model for the treatment of selected patients with painful musculoskeletal symptoms is offered. DISCUSSION: Important links exist between psychologic and social factors and recovery from insults to the "soft tissues." Identifiable biochemical and physiologic processes mediate this relationship. It is time to rethink and refine views of the role of psychologic and social factors in musculoskeletal illness, chronicity, and 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".