Pain and Poppies: The Good, the Bad, and the Ugly of Opioid Analgesics
Bibliographic record
Abstract
Treating pain is one of the most difficult challenges in medicine and a key facet of disease management. The isolation of morphine by Friedrich Sertürner in 1804 added an essential pharmacological tool in the treatment of pain and spawned the discovery of a new class of drugs known collectively as opioid analgesics. Revered for their potent pain-relieving effects, even Morpheus the god of dreams could not have dreamt that his opium tincture would be both a gift and a burden to humankind. To date, morphine and other opioids remain essential analgesics for alleviating pain. However, their use is plagued by major side effects, such as analgesic tolerance (diminished pain-relieving effects), hyperalgesia (increased pain sensitivity), and drug dependence. This review highlights recent advances in understanding the key causes of these adverse effects and explores the effect of chronic pain on opioid reward. SIGNIFICANCE STATEMENT: Chronic pain is pervasive and afflicts >100 million Americans. Treating pain in these individuals is notoriously difficult and often requires opioids, one of the most powerful and effective classes of drugs used for controlling pain. However, their use is plagued by major side effects, such as a loss of pain-relieving effects (analgesic tolerance), paradoxical pain (hyperalgesia), and addiction. Despite the potential side effects, opioids remain the pharmacological cornerstone of modern pain therapy. This review highlights recent breakthroughs in understanding the key causes of these adverse effects and explores the cellular control of opioid systems in reward and aversion. The findings will challenge traditional views of the good, the bad, and the ugly of opioids.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".