The addictive process of opioids: current and novel interventions in opioid use disorder
Bibliographic record
Abstract
The growing epidemic of opioid misuse presents numerous challenges for healthcare practitioners and patients alike as friction exists between ease of use and efficacy, and potential for overuse and addiction. With over 82 000 deaths related to opioid overdose in North America in 2020, it is imperative to gain a better understanding of the underlying mechanisms behind the addiction process, as well as the current methods being used in the arsenal against this disease. The current best pharmacological approaches for mediating opioid use disorder are methadone, buprenorphine, naltrexone, and naloxone, which act on opioid receptors to produce diverse effects based upon the patients' needs. The variety of effects that these drugs produce, which include removing opioid withdrawal, reversing overdose effects, and blocking opioid properties, makes this arsenal of therapeutics a global necessity in addressing the opioid use epidemic. Accordingly, this narrative review provides a summary of the available data regarding the physiological processes by which opioid addiction takes place and discusses the current and future potential of interventional methods used to mitigate opioid use disorder. The mechanisms of action and subsequent functional outcomes must be understood to reduce the number of opioid-related deaths worldwide.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".