Bibliographic record
Abstract
Nociceptive pain alerts the body to potential or actual tissue damage. By contrast, neuropathic pain, which results from injury or damage to the nervous system, persists long after all signs of the original injury have disappeared. This type of maladaptive pain presents a significant clinical problem, as it responds poorly or unpredictably to classical analgesics. There is also no single, uniformly well-tolerated drug that is reliably helpful. Current understanding of the etiology of neuropathic pain reveals seven potential targets for therapeutic intervention. These are: 1) ectopic activity in damaged peripheral nerves; 2) increased excitability in spinal dorsal horn neurons; 3) restoration or augmentation of GABAergic inhibition in the dorsal horn; 4) supraspinal and affective mechanisms; 5) alterations in the sympathetic nervous system; 6) spinal peptidergic mechanisms; and 7) spinal excitatory amino acid receptors. Current therapeutic approaches, using drugs such as gabapentin, anticonvulsants, ketamine or methadone, and potential new approaches are discussed in the context of these seven drug targets.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".