Dynamic Computational Models and Simulations of the Opioid Crisis: A Comprehensive Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Opioids have been shown to temporarily reduce the severity of pain when prescribed for medical purposes. However, opioid analgesics can also lead to severe adverse physical and psychological effects or even death through misuse, abuse, short- or long-term addiction, and one-time or recurrent overdose. Dynamic computational models and simulations can offer great potential to interpret the complex interaction of the drivers of the opioid crisis and assess intervention strategies. This study surveys existing studies of dynamic computational models and simulations addressing the opioid crisis and provides an overview of the state-of-the-art of dynamic computational models and simulations of the opioid crisis. This review gives a detailed analysis of existing modeling techniques, model conceptualization and formulation, and the policy interventions they suggest. It also explores the data sources they used and the study population they represented. Based on this analysis, direction and opportunities for future dynamic computational models for addressing the opioid crisis are suggested.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it