Response to <scp>H</scp>ser <i>et al</i>. (2014): The necessity for more and better data on the global epidemiology of opioid dependence
Bibliographic record
Abstract
heroin is still the most commonly used opioid and therefore responsible for the majority of overdose mortality.These are important distinctions that have implications for development of region-or population-specific interventions that address local variations in patterns of opioid use, health-care system organization and capacity, and factors that facilitate or inhibit treatment seeking.Opioid addiction clearly is a global problem, and global challenges call for global strategies.The bottom line is: how can we eliminate or reduce the global burden of opioid dependence?Despite an intense and decades-long scientific effort that has provided treatment options and ways to effectively treat and manage opioid addiction and HIV, the global DALY has increased considerably over the past 10 years.Even in high-income countries where opioid addiction treatment and general health-care is more available and accessible, several of those countries were among the ones with highest DALYs.Obviously, the problem is complex.Is it that more people became dependent on opioids, more people died of opioid dependence, more people were living with disability, or a combination of these or other reasons?Answers to these questions will suggest courses of action to take.Given the regional variation suggested by Degenhardt et al.'s findings, global strategies to effectively reduce DALYs will need to be tailored to local context, taking into consideration the local patterns of opioid use and addiction, drug policies and interventions, and available treatment and public health resources.Nevertheless, if scientific-based interventions and practices are effective in addressing opioid addiction and problems, a starting-point will be to ask: are they widely adopted, well implemented and readily accessible?It is apparently not the case in resource-limited regions, and even questionable among low-income individuals in high-income countries.Given the current understanding of the long-term course of opioid addiction and its tremendous burden, it is crucially urgent to implement known effective strategies globally and continue to search for or develop better ways to reduce the global burden of opioid dependence.
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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 | 0.008 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.026 | 0.020 |
| Insufficient payload (model declined to judge) | 0.068 | 0.029 |
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".