Neuropathic pain and the electrophysiology and pharmacology of nerve injury
Bibliographic record
Abstract
Abstract Nociceptive pain serves the useful purpose of alerting the body to potential or actual tissue damage. By contrast, neuropathic pain that results from injury or damage to the nervous system persists long after all signs of the original injury have disappeared. Neuropathic pain presents a significant clinical problem as it responds poorly to classical analgesics such as non‐steroidal anti‐inflammatory drugs (NSAIDs) and to opioids; there is also no single, uniformly well‐tolerated drug that is reliably helpful. Treatment currently involves the use of anticonvulsant and/or antidepressant drugs. Electrophysiological experiments on dorsal root ganglion and spinal cord neurons of nerve‐injured experimental animals are yielding new information on the pathophysiology of neuropathic pain. Analysis of actions of various neuropeptides and neurotransmitters in these models has helped to explain the poor efficacy of opioids and suggests new therapeutic approaches to the management of neuropathic pain. Drugs that stimulate α2‐c‐adrenoceptors or that mimic the actions of neuropeptide Y, galanin, or the opioid‐like peptide, nociceptin, may be of use in this regard. Drug Dev. Res. 54:140–153, 2002. © 2002 Wiley‐Liss, Inc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".