Emergency Medicine and Rapid Response Virtual Team (EM-RRVT): Insights from the Hajj 1445/2025 Experience
Bibliographic record
Abstract
Introduction: The use of telemedicine in acute care settings has not been investigated in large mass gatherings. Further exploration is needed to determine its efficacy, applicability and to explore challenges and opportunities. Aim: This study aims to evaluate a pilot deployment of virtual emergency team during one of the largest mass gatherings in the world, the Hajj religious season. Methods: The Emergency Medicine and Rapid Response Virtual Team (EM-RRVT) was launched from June 13 to July 6, 2024. The pilot was conducted in phases, with Phase One occurring in Mecca from June 13 to June 19. Subsequently, the team was activated on an on-demand basis. The team's role was to complement the on-ground teams as well as various emergency departments. Results: The team encountered a total of 324 patients from 20 countries, with a variety of medical conditions. The most prevalent condition was acute coronary syndrome, followed by trauma, within the peak hours from 10:00 to 22:00 coinciding with the movement of pilgrims. Over half of the patients were treated and released back to their Hajj contingents during surge hours. Conclusion: The use of telemedicine in an acute setting showed promising results. The establishment of the Emergency Medicine and Rapid Response Virtual Team proved to be both feasible and applicable. The scalability and flexibility of the service contributed to its efficiency.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".