Bibliographic record
Abstract
Neuropathic pain is a personally devastating and costly condition affecting 3-8% of the population. Existing treatments have limited effectiveness and produce relatively frequent adverse effects. Preclinical research has identified many promising pharmacological targets; however, reliable predictors of success in humans remain elusive. At least 50 new molecular entities have reached clinical development including: glutamate antagonists, cytokine inhibitors, vanilloid-receptor agonists, catecholamine modulators, ion-channel blockers, anticonvulsants, opioids, cannabinoids, COX inhibitors, acteylcholine modulators, adenosine receptor agonists and several miscellaneous drugs. Eight drugs are in Phase III trials at present. Strategies that may show promise over existing treatments include topical therapies, analgesic combinations and, in future, gene-related therapies. Recent years have heralded an explosion of pharmaceutical development in neuropathic pain, reflecting advanced knowledge of neurobiology and a heightened perception of the commercial value of neuropathic pain therapeutics. In the interest of improving patient care, the authors recommend implementing comparative studies throughout the development process in order to demonstrate the increased value of novel agents.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".