Virtual overdose monitoring services/mobile overdose response services: estimated number of potentially averted drug poisoning fatality events by various telephone and digital-based overdose prevention/harm reduction services in North America
Bibliographic record
Abstract
Background: Virtual overdose monitoring services or Mobile Overdose Response Services (MORS) are novel virtual harm reduction tools which have gained popularity as an adjunct public health intervention especially for those who cannot access harm reduction resources through traditional means. At this time, relatively little is known about their ability to reach their goals of reducing overdose mortality. Our study aims to summarize the potential effectiveness of various MORS collectively to avoid potential mortality from a drug poisoning event/drug overdose. Methods: Utilizing publicly available data from various MORS alongside some usage data provided by these services for this study, we model the impact of these services on fatal drug poisoning/overdose. In order to calculate the number of deaths averted, a Monte Carlo simulation was used to calculate point estimates with 95% confidence for fatal drug poisonings/drug overdose potentially averted through the utilization of various MORS. Results: From the earliest mention of MORS in current literature (2019), a total of 299 drug poisoning/overdose events occurred across these services. Noting the broad range of mortality statistics available in current literature, these technologies have potentially prevented between 33 to 243 deaths. Our Monte Carlo estimates 135 potentially fatal drug poisonings/overdose were overall averted by the various MORS. Conclusions: While there is yet to be a robust data set proving the effectiveness of these services, conservative estimates show that MORS can reduce mortality associated with substance use and therefore should be considered as a viable harm-reduction strategy but as an adjunct to more established harm reduction services such as supervised consumption sites and supervised injection facilities. While more research is needed, clinicians and practitioners should consider the suggestion of these tools for patients who use drugs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".