Bibliographic record
Abstract
OBJECTIVE: The purpose of this review was to determine how effective different classes of analgesic agents are in the management of chronic pain. METHODOLOGY: The literature search identified five systematic reviews and 18 randomized controlled trials to provide evidence about systemic drug treatment for chronic pain. RESULTS: Studies in the systematic reviews were mainly of low back pain, and studies in the randomized controlled trials were mainly of fibromyalgia. Other studies investigated of rheumatic pain, musculoskeletal pain, chronic low back pain, and temporomandibular pain. Classes of analgesic agents reviewed were antidepressants, nonsteroidal anti-inflammatory drugs, muscle relaxants, opioid analgesics, and a number of miscellaneous agents. CONCLUSIONS: For chronic pain, opioid analgesics provide benefit for up to 9 weeks (level 2). For chronic low back pain, the evidence shows that various types of nonsteroidal antiinflammatory drugs are equally effective or ineffective, and that antidepressants provide no benefit in the short to intermediate term (level 2). Muscle relaxants showed limited effectiveness (level 3) for chronic neck pain and for chronic low back pain for up to 4 weeks. For fibromyalgia, there is limited evidence (level 3) of the effectiveness of amitryptiline, ondansetron, zoldipem, or growth hormone, and evidence of no effectiveness for nonsteroidal anti-inflammatory drugs, malic acid with magnesium, calcitonin injections, or s-adenyl-L-methionine. For temporomandibular pain, oral sumatriptan is not effective (level 2). The remaining evidence was inadequate (level 4a) or contradictory (level 4b).
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".