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Record W4200082032 · doi:10.1161/circ.144.suppl_2.9876

Abstract 9876: Optimizing Public Naloxone Kit Locations Through Mathematical Modeling

2021· article· en· W4200082032 on OpenAlexaffabout
Kwan Leung, Brian Grunau, May K. Lee, Jane A. Buxton, Jennie Helmer, Sean van Diepen, Jim Christenson, Timothy M. Chan

Bibliographic record

VenueCirculation · 2021
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of AlbertaIsland HealthUniversity of British ColumbiaBC Centre for Disease ControlUniversity of Toronto
Fundersnot available
Keywords(+)-NaloxoneOpioid overdoseMedicineMcNemar's testPharmacyMedical emergencyEmergency medicinePublic healthDrug overdoseExpanded accessOpioidPoison controlFamily medicineNursingInternal medicineStatistics

Abstract

fetched live from OpenAlex

Introduction: Use of bystander-administered naloxone may lead to improved likelihood of recovery from opioid overdose. We sought to determine the accessibility of public access naloxone kits on nearby opioid overdose incidents if placed at public transit stops, compared to placing kits outside pharmacies or with existing public access automated external defibrillators (PADs). Methods: We included all incidents in Metro Vancouver, British Columbia responded to by British Columbia Emergency Health Services coded as a drug overdose with naloxone administered on-scene (Dec. 2014 to Aug. 2020). We geo-coded all public transit bus stops and used a mathematical optimization model to select bus stops where publicly accessible naloxone kits could be placed to maximize accessibility (defined as ≤100 m walking distance) to opioid overdoses. We evaluated accessibility on out-of-sample OHCAs using five-fold cross validation and compared against two baseline policies: placing publicly accessible naloxone kits at all pharmacies identified by the College of Pharmacists of British Columbia, and placing kits at all PADs identified by the British Columbia AED Registry. Statistical analysis was conducted using McNemar’s test. Results: We identified 14,318 opioid overdoses, 8,972 bus stops, 736 pharmacies, and 425 PADs. Accessibility of public naloxone kits for opioid overdose locations was 5.1% when placed at all pharmacies and 3.5% when placed with all existing PADs. Optimized naloxone kit placement using bus stops as candidate locations resulted in significantly higher accessibility than both pharmacy and PAD-based placement at 14.8% with 10 optimized locations (P<0.001), increasing to 36.7% with 500 locations (P<0.001). Conclusion: Optimizing placement of public access naloxone kits at select public transit locations can provide significantly higher accessibility to opioid overdose locations compared to placement at pharmacies or at existing PAD locations.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.060
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.071
GPT teacher head0.313
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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