Bibliographic record
Abstract
Neuropathic pain affects 2 to 3% of the population in developed countries and can be particularly severe and debilitating. There has been considerable controversy regarding the role of opioid analgesics in the management of this disabling condition. However, a recent systematic review of high‐quality randomized controlled trials (RCTs) utilizing opioid analgesics in the treatment of chronic neuropathic pain showed clinically significant benefit. These studies demonstrate, on average a 20 to 30% reduction in pain intensity. RCTs in patients with postherpetic neuralgia given controlled‐release oxycodone or controlled‐release morphine showed a significant reduction in pain intensity with variable improvement in sleep and disability. Trials of controlled‐release oxycodone in painful diabetic neuropathy showed more consistent improvement in pain, sleep and ability to function. Nausea and constipation are common side effects, but can usually be controlled with anti‐emetics and a bowel regimen, respectively. Psychological dependence or addiction is unusual in the absence of a history of substance abuse. Methadone may be particularly useful when conventional opioid analgesics have failed due to its N‐methyl‐D‐aspartate (NMDA) antagonist properties. When antidepressants and anticonvulsants fail to provide adequate pain control for neuropathic pain, opioid analgesics are emerging as an important treatment option ‐ in some cases, this class of drugs can make the difference between bearable and unbearable pain.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".