Are we missing the opioid consumption in low- and middle-income countries?
Bibliographic record
Abstract
OBJECTIVES: The rise in opioid prescriptions with a parallel increase in opioid use disorders remains a significant challenge in some developed countries (opioid epidemic). However, little is known about opioid consumption in low- and middle-income countries (LMICs). In this short report, we aim to discuss the increase in opioid consumption in LMICs by providing an update on the opioid perspective in Brazil. METHODS: We analyzed opioid sales on the publicly available Brazilian Health Regulatory Agency (ANVISA) database from 2015 to 2020. RESULTS: In Brazil, opioid sales increased 34.8 %, from 8,839,029 prescriptions in 2015 to 11,913,823 prescriptions in 2020, this represents an increase from 44 to 56 prescriptions for every 1,000 inhabitants. Codeine phosphate combined with paracetamol and tramadol hydrochloride were the most common opioids prescribed with an increase each year. CONCLUSIONS: The results suggest that opioid prescriptions are rising in Brazil in a 5 years period. Brazil may have a unique opportunity to learn from other countries and develop consistent policies and guidelines to better educate patients and prescribers and to prevent an 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".