Effects and consequences of nerve injury on the electrical properties of sensory neurons
Bibliographic record
Abstract
Nociceptive pain alerts the body to potential or actual tissue damage. By contrast, neuropathic or "noninflammatory" pain, which results from injury to the nervous system, serves no useful purpose. It typically continues for years after the original injury has healed. Sciatic nerve lesions can invoke chronic neuropathic pain that is accompanied by persistent, spontaneous activity in primary afferent fibers. This activity, which reflects changes in the properties and functional expression of Na+, K+, and Ca2+ channels, initiates a further increase in the excitability of second-order sensory neurons in the dorsal horn. This change persists for many weeks. The source of origin of the pain thus moves from the peripheral to the central nervous system. We hypothesize that this centralization of pain involves the inappropriate release of peptidergic neuromodulators from primary afferent fibers. Peptides such as substance P, neuropeptide Y (NPY), calcitonin-gene-related peptide (CGRP), and brain-derived neurotrophic factor (BDNF) may promote enduring changes in excitability as a consequence of neurotrophic actions on ion channel expression in the dorsal horn. Findings that form the basis of this hypothesis are reviewed. Study of the neurotrophic control of ion channel expression by spinal peptides may thus provide new insights into the etiology of neuropathic 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".