MétaCan
Menu
Back to cohort
Record W2802079127 · doi:10.1136/bmjopen-2018-ems.82

82 Spatiotemporal aed optimisation is generalizable

2018· article· pl· W2802079127 on OpenAlex
CLF Sun, LIM Karlsson, Christian Torp‐Pedersen, Fredrik Folke, TCY Chan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAbstracts · 2018
Typearticle
Languagepl
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
FundersDanish Foundation TrygFondenTrygFonden
KeywordsGeneralizability theoryMedicineAutomated external defibrillatorSoftware deploymentMcNemar's testMedical emergencyComputer scienceStatisticsEmergency medicine

Abstract

fetched live from OpenAlex

<h3>Aim</h3> Mathematical optimisation of automated external defibrillator (AED) placements has the potential to improve out-of-hospital cardiac arrest (OHCA) coverage and reverse the negative effects of limited AED accessibility. However, the generalizability of optimisation approaches has not yet been investigated. <h3>Method</h3> We examined the performance and generalizability of a spatiotemporal AED placement optimisation methodology, initially developed for Toronto, Canada,<sup>1</sup> to the new study setting of Copenhagen, Denmark. We identified all atraumatic treated public OHCAs (1994–2016) and all registered AEDs (2016) in Copenhagen, Denmark. We then calculated the coverage loss associated with limited temporal accessibility of registered AEDs, and used a spatiotemporal optimisation model to quantify the potential coverage gain of optimised AED deployment. Coverage gain of spatiotemporal deployment over a spatial-only solution was quantified through 10-fold cross-validation. Statistical testing was performed using χ2 and McNemar’s tests. <h3>Results</h3> We identified 2149 public OHCAs and 1573 registered AED locations. Coverage loss was found to be 24.4% (1,104 OHCAs covered under assumed 24/7 coverage, and 835 OHCAs under actual coverage). The relative coverage gain from using the spatiotemporal model over a spatial-only approach was 15.3%. Temporal and geographical trends in coverage gain were similar to Toronto. <h3>Conclusion</h3> Without modification, a previously developed spatiotemporal AED optimisation approach was applied to Copenhagen, resulting in similar OHCA coverage findings as Toronto, despite large geographic and cultural differences between the two cities. In addition to reinforcing the importance of temporal accessibility of AEDs, these similarities demonstrate the generalizability of optimisation approaches to improve AED placement and accessibility. <h3>Reference</h3> . Sun CLF, Demirtas D, Brooks SC, Morrison LJ, Chan TCY. Optimising public defibrillator deployment to overcome spatial and temporal accessibility barriers. Journal of the American College of Cardiology2016. <h3>Conflict of interest</h3> None <h3>Funding</h3> This work was funded by the ZOLL Foundation (ZOLL Foundation Research Grant) and supported by the Danish foundation TrygFonden with no commercial interest in the field of cardiac arrest.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.638
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.005

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 head0.024
GPT teacher head0.296
Teacher spread0.273 · 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