Bibliographic record
Abstract
The role of antidepressants and anticonvulsants in the management of neuropathic pain has been well established. However, up to 50% of patients obtain inadequate pain relief with the use of either or both of these agents; in this subpopulation, an opioid analgesic may be beneficial. There is clear evidence that opioid analgesics are efficacious in the management of neuropathic pain, but there is controversy as to the balance between analgesia and adverse effects. Opioid treatment may require higher doses than other kinds of drug therapies, thereby increasing the risk of opioid‐related side effects. Psychological dependence or addiction, however, is not usually an issue in pain management with opioid analgesics. The extant literature strongly suggests the trial of an opioid analgesic in the management of neuropathic pain if adjuvant analgesics fail to provide adequate pain control. Failure of one opioid warrants a trial of another opioid because their effectiveness can vary among patients; the results are based on physiochemical properties of the drug and idiosyncratic reactions of the patient. Neuropathic pain can be a difficult problem to manage, and sometimes the use of an opioid analgesic can make the difference between bearable and unbearable pain so that patients can get on with their lives.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".