Aperçu - Que peuvent nous révéler les données des ambulanciers paramédicaux sur la crise des opioïdes au Canada? At-a-glance - What can paramedic data tell us about the opioid crisis in Canada?
Bibliographic record
Abstract
The nature of Canada's opioid crisis necessitates additional data sources that can provide a more comprehensive picture of the epidemic, in order to provide public health officials and decision-makers with a robust evidence base. Paramedic data provide a conduit into the community where overdoses occur. Prehospital events and circumstances surrounding opioid-related overdoses provide unique opportunities to collect evidence that can contribute to prevention, harm reduction and health promotion efforts. Using data extracted from the Ottawa Paramedic Service (OPS), this proof-of-concept study demonstrated that paramedic response data were useful in providing near real-time epidemiological information (person, time and place) on the opioid epidemic and in assessing trends and opportunities to develop alert triggers. Between January and June 2017, the OPS responded to an average of four opioid-related calls each week. On average, 0.5 mg of naloxone was administered each time. For the study period, linear trends show a small but insignificant increase in calls (p = 0.18). A higher volume of calls occurred between April 16 and 29, 2017. According to local media reports, this spike in paramedic responses was due to the arrival of high-grade fentanyl in Ottawa. With further validation, paramedic data can potentially provide a novel data source to monitor opioid-related overdoses.RESUME: La nature de la crise des opioides au Canada necessite des sources de donnees supplementaires aptes a dresser un portrait plus fidele de l’epidemie, afin de fournir aux responsables en sante publique et aux decideurs une base de donnees probantes solide. Les donnees des ambulanciers paramedicaux sont un point d’acces aux collectivites ou les surdoses surviennent. Les evenements prehospitaliers et les circonstances entourant les surdoses d’opioides offrent des occasions uniques de recueillir des donnees probantes pouvant contribuer a la prevention, a la reduction des mefaits et aux efforts de promotion de la sante. A l’aide de donnees extraites du Service paramedic d’Ottawa (SPO), cette etude de validation de principe a demontre que les donnees d’intervention ambulanciere paramedicale etaient utiles pour obtenir des renseignements epidemiologiques en temps quasi reel (personne, heure et lieu) sur l’epidemie d’opioides et pour evaluer les tendances ainsi que les possibilites d’elaborer des declencheurs d’alerte. Entre janvier et juin 2017, le SPO a repondu a une moyenne de quatre appels lies aux opioides par semaine. A chaque fois, 0,5 mg de naloxone ont en moyenne ete administres. Pour la periode a l’etude, les tendances lineaires montrent une faible augmentation des appels, non significative (p = 0,18). Le volume d’appels a augmente entre le 16 et le 29 avril 2017. Selon les medias locaux, ce pic dans les interventions ambulancieres paramedicales est attribuable a l’arrivee de fentanyl de qualite superieure a Ottawa. Avec une validation plus poussee, ces donnees paramedicales pourraient potentiellement constituer une nouvelle source de donnees pour la surveillance des surdoses liees aux opioides.
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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 | 0.016 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".