Bibliographic record
Abstract
The opioid crisis in the USA and in other developed countries can potentially affect low- and middle-income countries (LMICs). The licit medical use of opioids has two sides. The USA and high-income countries maintain abundant supply for medical prescription. Between 1990 and 2010, the use of opioids for cancer pain relief was overtaken by a dramatic rise in the opioid prescriptions for non-cancer acute or chronic pain. The surge led to the opioid epidemic, recognized as social catastrophe in the USA, Canada and in some countries in Europe. From 2016, the medical community, health policy regulators and law-makers have taken actions to tackle this opioid crisis. On the other side, formulary deficiency and low opioid availability exists for three-fourths of the global population living in LMICs. Physicians and nurses in Asia and Africa engaged in cancer pain relief and palliative care face a constant paucity of opioids. Millions of patients in LMICs, suffering from life-modifying cancer pain, do not have access to morphine and other essential opioids, due to restrictive opioid policies. Attention will be needed to improve opioid availability in large parts of the world, even though the opioid crisis has led to control the licit medical use in the USA.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 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".