Overdosing in a Motor Vehicle
Bibliographic record
Abstract
BACKGROUND: Fentanyl, a type of opioid, in impaired driving cases increased across cities in the United States. OBJECTIVES: No empirical studies have examined motor vehicle overdoses with fentanyl use. We investigated the magnitude of the motor vehicle overdose problem in Providence, RI, and the environmental, socioeconomic, and geographic conditions associated with motor vehicle overdose occurrence. METHODS: This was a retrospective observational study of emergency medical services data on all suspected opioid overdoses between January 1, 2017, and October 31, 2020. The data contain forced-choice fields, such as age and biological sex, and an open-ended narrative in which the paramedic documented clinical and situational information. The overdoses were geocoded, allowing for the extraction of sociodemographic data from the U.S. Census Bureau's American Community Survey. Seven other data sources were included in a logistic regression to understand key risk factors and spatial patterns of motor vehicle overdoses. RESULTS: Of the 1,357 opioid overdose cases in this analysis, 15.2% were defined as motor vehicle overdoses. In adjusted models, we found a 61% increase in the odds of a motor vehicle overdose involvement for men versus women, a 16.8% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to the nearest gas station, and a 10.7% decrease in the odds of a motor vehicle overdose for a one-unit increase in distance to a buprenorphine clinic. CONCLUSION: There is a need to understand the interaction between drug use in vehicles to design interventions for decreasing driving after illicit drug use.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".