Ineffectiveness of Paramedic Naloxone Administration as a Standalone Metric for Community Opioid Overdoses and the Increasing Use of Naloxone by Community Members
Bibliographic record
Abstract
INTRODUCTION: With Canada's growing opioid crisis, many communities are attempting to monitor cases in real-time. Paramedic Naloxone Administration (PNA) has become a common metric for monitoring overdoses. We evaluate whether the use of naloxone administration counts represents an effective monitoring tool for community opioid overdoses. METHODS: The electronic ambulance call report database of Peterborough Paramedics (Ontario, Canada) was examined. De-identified records from 2016-2019 with problem codes of "Opioid Overdose", along with all patients documented as receiving naloxone were extracted. Chi-square and Bonferroni-adjusted post hoc proportion tests were used for comparison of counts. RESULTS: 558 opioid overdoses were identified, 124 (22%) of which had PNA documented, 181(32%) had naloxone prior to arrival documented and 264 (47%) received no naloxone. Over the three years, the annual number of overdose cases increased, while the proportion of patients receiving PNA decreased significantly each year. PNA was also associated with calls in a residence. Naloxone was administered by a non-paramedic in 262 cases, with 181 of these identified as opioid overdoses and was more common in later years and in cases occurring in public places. CONCLUSION: PNA calls did not account for a significant percentage of opioid overdoses attended to by paramedics. The strong association between PNA and call location being a residence, along with increasing use of community naloxone kits, may cause certain populations to be under-represent if PNA is used as a standalone metric. The decreasing association with time may also lead to a falsely improving metric further reducing its effectiveness. Thus, PNA when used alone may no longer be a suitable metric for opioid overdose tracking.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".