82 Spatiotemporal aed optimisation is generalizable
Bibliographic record
Abstract
Aim 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. Method We examined the performance and generalizability of a spatiotemporal AED placement optimisation methodology, initially developed for Toronto, Canada,1 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. Results 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. Conclusion 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. Reference . 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. Conflict of interest None Funding 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.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".