Potential Role of Insulin-Like Growth Factors in Myofascial Pain Syndrome
Bibliographic record
Abstract
ABSTRACT: Insulin-like growth factors have diverse functions in skeletal muscles by acting through multiple signaling pathways, including growth regulation and differentiation, anti-inflammation, and antioxidation. Insulin-like growth factors have anti-inflammatory effects and also play roles in nociceptive pathways, determining pain sensitivity, in addition to their protective role against ischemic injury in both the nervous system and skeletal muscle. In skeletal muscle, insulin-like growth factors maintain homeostasis, playing key roles in maintenance, accelerating muscle regeneration, and repair processes. As part of their maintenance role, increased levels of insulin-like growth factors may be required for the repair mechanisms after exercise. Although the role of insulin-like growth factors in myofascial pain syndrome is not completely understood, there is evidence from a recent study that insulin-like growth factor 2 levels in patients with myofascial pain syndrome are lower than those of healthy individuals and are associated with increased levels of inflammatory biomarkers. Importantly, higher insulin-like growth factor 2 levels are associated with increased pain severity in myofascial pain syndrome patients. This may suggest that too low or high insulin-like growth factor levels may contribute to musculoskeletal disorder process, whereas a midrange levels may optimize healing without contributing to pain hypersensitivity. Future studies are required to address the mechanisms of insulin-like growth factor 2 in myofascial pain syndrome and the optimal level as a therapeutic agent.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".