Weighing the balance: how analgesics used in chronic pain influence sleep?
Bibliographic record
Abstract
Pain and sleep share a bidirectional relationship, with each influencing the other. Several excellent reviews have explored this relationship. In this article, we revisit the evidence and explore existing research on this complex inter-relationship. The primary focus of the article is on the pharmacological treatment of chronic non-malignant pain and the main purpose is to review the effect of various pharmacological agents used in the management of chronic pain on sleep. This has not been comprehensively done before. We explore the clinical use of these agents, their impact on sleep architecture and sleep physiology, the mechanism of action on sleep parameters and sleep disorders associated with these agents. Pharmacological classes reviewed include antidepressants, opioid analgesics, anti-epileptics, cannabinoids and non-steroidal anti-inflammatory agents, drugs most commonly used to manage chronic pain. The objective is to help health professionals gain better insight into the complex effect that commonly used analgesics have on an individual's sleep and how this could impact on the effectiveness of the drug as an analgesic. We conclude that antidepressants have both positive and negative effects on sleep, so do opioids, but in the latter case the evidence shifts towards the counterproductive side. Some anticonvulsants are sleep sparing and non-steroidal anti-inflammatory drugs (NSAIDs) are sleep neutral. Cannabinoids remain an underexplored and researched group.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".