Spatiotemporal AED Location Optimization
Bibliographic record
Abstract
Background: Mathematical optimization can be used to plan future AED placement to maximize out-of-hospital cardiac arrest (OHCA) coverage. Many public access AEDs are placed in locations without 24/7 access. AED coverage can be overestimated unless temporal availability is considered. Objective: To develop a new spatiotemporal AED location optimization model that accounts for both spatial and temporal information. Methods: We identified all atraumatic public-location OHCAs occurring in Toronto, Canada from Jan. 2006 – Aug. 2014. We gathered location and operating hours data for 4898 buildings that were used as potential sites for AED placement. We extended a previously published spatial optimization model, which identifies locations to place AEDs that maximize the number of historical OHCAs occurring within 100 m of an AED. The new spatiotemporal model finds AED locations that maximize the number of OHCAs occurring within 100 m of an available AED, considering when the OHCAs occurred (“actual coverage”). We then compared the spatial and spatiotemporal models on actual coverage of out-of-sample OHCAs using 10-fold cross validation. Statistical analysis was performed using McNemar’s test. Results: We identified 2440 atraumatic public-location OHCAs. AED locations chosen by the spatiotemporal model outperformed those chosen by the spatial model by 26.1% in actual coverage (p<0.001). The figure shows coverage improvement at all times of day: daytime (11.2%), evening (37.4%), and night (292.3%). Equivalently, 40.2% fewer AEDs are needed when using the spatiotemporal model to reach the same level of actual coverage provided by AEDs located according to the spatial model. Conclusion: Spatiotemporal optimization can maximize actual OHCA coverage by accounting for AED availability when identifying future AED locations. The largest gains occurred during the evening and night, which is when the largest coverage losses were experienced by Toronto’s existing AEDs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".