Modulation of Pathological Pain by Epidermal Growth Factor Receptor
Bibliographic record
Abstract
Chronic pain has been widely recognized as a major public health problem that impacts multiple aspects of patient quality of life. Unfortunately, chronic pain is often resistant to conventional analgesics, which are further limited by their various side effects. New therapeutic strategies and targets are needed to better serve the millions of people suffering from this devastating disease. To this end, recent clinical and preclinical studies have implicated the epidermal growth factor receptor signaling pathway in chronic pain states. EGFR is one of four members of the ErbB family of receptor tyrosine kinases that have key roles in development and the progression of many cancers. EGFR functions by activating many intracellular signaling pathways following binding of various ligands to the receptor. Several of these signaling pathways, such as phosphatidylinositol 3-kinase, are known mediators of pain. EGFR inhibitors are known for their use as cancer therapeutics but given recent evidence in pilot clinical and preclinical investigations, may have clinical use for treating chronic pain. Here, we review the clinical and preclinical evidence implicating EGFR in pathological pain states and provide an overview of EGFR signaling highlighting how EGFR and its ligands drive pain hypersensitivity and interact with important pain pathways such as the opioid system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".