Abstract 108: Optimal In-Hospital Defibrillator Placement
Bibliographic record
Abstract
Introduction: Delays in defibrillation of in-hospital cardiac arrests (IHCAs) can reduce the likelihood of survival. Mathematical optimization has been shown to improve public location defibrillator placement but has not been applied to in-hospital defibrillator placement. Objective: To determine if mathematical optimization of in-hospital defibrillator placements can reduce distances to IHCAs compared to current placements in a large academic teaching hospital. Methods: We identified all treated IHCAs and defibrillator placements in St. Michael’s Hospital in Toronto, Canada from Jan. 2007 to Jun. 2017 and mapped them to a 3-D representation of the hospital that we developed from blueprints. An equal number of optimal defibrillator locations was identified using a mathematical optimization model that minimizes the average distance between IHCAs and the closest defibrillator in a 10-fold cross-validation approach. The optimized and current defibrillator locations were compared in terms of average distance to the out-of-sample IHCAs in each fold. We repeated the analysis excluding IHCAs and defibrillators in intensive care units (ICUs), operating theaters (OTs), and the emergency department (ED). Significance in the difference of average distance was determined using a Wilcoxon signed-rank test. Results: We identified 537 treated IHCAs and 53 defibrillators within the hospital during the study period. Of these, 236 IHCAs and 38 defibrillators were outside of ICUs, OTs, and the ED. Optimal defibrillator placements reduced the average defibrillator-to-IHCA distance from 17.1 m to 3.8 m, a relative decrease of 77.8% (P<0.01) on all IHCAs compared to current defibrillator placements. For non-ICU/OT/ED IHCAs, the average distance was reduced from 18.3 m to 9.8 m, a relative decrease of 46.4% (P<0.01). Conclusion: Optimization-guided placement of in-hospital defibrillators can significantly reduce the distance from an IHCA to the closest defibrillator.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".