Bibliographic record
Abstract
Opioid use increased dramatically in the 1990s upon introduction of newer, more relaxed regulations. As opioid prescriptions for pain increased, a parallel increase in opioid abuse and addiction occurred; this phenomenon is widely known as the opioid crisis. Cannabis had long been considered a recreational drug until legislation in 2001 allowed highly limited access to the drug for medicinal purposes. Although small-scale clinical trials show promising results for the use of cannabis in pain management, it is not currently indicated for chronic or severe-to-moderate acute pain, for which opioids are typically considered the standard of care. The impending legalization of recreational cannabis may mark a turning point in pain medicine as the general public becomes able to selfmedicate with cannabis. This increased availability may lead individuals prescribed opioids to combine or replace them with cannabis, with potential positive impacts. There is growing evidence that cannabinoid compounds present in cannabis are able to augment opioid-induced pain relief. Increased availability of cannabis is linked to decreased opioid-related mortality and hospitalizations; furthermore, cannabis might act as a tool to treat opioid addiction. Cannabis does possess adverse effects and addiction risk, and expanded research into its properties is needed. However, its relatively decreased risk profile and potential positive effects indicate that it may serve an important role in addressing the opioid crisis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".