Target Receptors of Regenerating Nerves: Neuroma Formation and Current Treatment Options
Bibliographic record
Abstract
Neuromas form as a result of disorganized sensory axonal regeneration following nerve injury. Painful neuromas lead to poor quality of life for patients and place a burden on healthcare systems. Modern surgical interventions for neuromas entail guided regeneration of sensory nerve fibers into muscle tissue leading to muscle innervation and neuroma treatment or prevention. However, it is unclear how innervating denervated muscle targets prevents painful neuroma formation, as little is known about the fate of sensory fibers, and more specifically pain fiber, as they regenerate into muscle. Golgi tendon organs and muscle spindles have been proposed as possible receptor targets for the regenerating sensory fibers; however, these receptors are not typically innervated by pain fibers, as these free nerve endings do not synapse on receptors. The mechanisms by which pain fibers are signaled to cease regeneration therefore remain unknown. In this article, we review the physiology underlying nerve regeneration, the guiding molecular signals, and the target receptor specificity of regenerating sensory axons as it pertains to the development and prevention of painful neuroma formation while highlighting gaps in literature. We discuss management options for painful neuromas and the current supporting evidence for the various interventions.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".