Late-Breaking Clinical Trial Abstracts
Bibliographic record
Abstract
Background: Cities around the world have underground or above-ground enclosed networks for pedestrian travel, representing unique environments for studying out-of-hospital cardiac arrest (OHCA) and resuscitation. The characteristics of OHCAs that occur in such networks are unknown. Objective: To determine whether cardiac arrests occurring in enclosed pedestrian networks are different from those in the encompassing city, using the PATH network in Toronto, the largest underground shopping complex in the world, as a model site. Methods: We identified all atraumatic, public-location OHCAs in Toronto from Apr. 2006 – Mar. 2015, and classified them according to location: Toronto, downtown, and PATH-accessible. PATH-accessible OHCAs are those that occur within the PATH network between the first underground and second above-ground floor. We collected demographic, prehospital intervention, and survival data for each OHCA. Statistical analysis was performed using t-tests and chi-squared tests. Results: We identified 2621 atraumatic public OHCAs, of which 521 were in downtown and 50 were PATH-accessible. Compared to Toronto overall, PATH-accessible OHCAs had significantly higher proportions of bystander witnessed interventions, initial shockable rhythm, and overall survival, with all differences being statistically significant. Similar significant differences were observed when comparing PATH-accessible to downtown OHCAs. There were no significant differences in demographics and survival among patients with initial shockable rhythm. Conclusion: This study suggests that OHCAs in enclosed pedestrian networks are uniquely different from other public settings. Bystander resuscitation efforts are significantly more frequent and survival rates are higher. Urban planners in similar networks worldwide should consider these findings when deciding on AED placement and how to cue bystander response.
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 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.083 | 0.012 |
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".