Event Medical Life Support (EMLS): Event Medicine for Multidisciplinary Teams
Bibliographic record
Abstract
Background: A Mass Gathering (MG) World Health Organization (WHO) definition is an occasion, either organized or spontaneous, where the "number of people attending is sufficient to strain the planning and response resources of the community, city, or nation hosting the event" (WHO, 2008).It can be planned or spontaneous, which can bring their own unique challenges to public health and other risks.Addis Ababa in Ethiopia has held the 13th INDEPTH ISC/AGM International Conference, which has brought together 350 participants from HDSS in the world from 22 countries, 38 HDSS leaders, INDEPTH board members, INDEPTH scientific committee, editors of the Lancet, editors of Global Public health, chair-person of the African Public Health Association, and 14 university presidents or vice-presidents which do not have HDSS.Six university presidents and/or vice presidents where the six HDSS in Ethiopia located, Representatives of Embassies, Save the Children, WHO, and key researchers from Stanford University have also been among the participants; hundreds of local scientific communities were all in attendance.Methods: A total of three Emergency Medicine and Critical Care residents based in Addis Ababa University and one consultant where involved.A duty room fully equipped of emergency drugs and other equipments were ready.Prehospital transportation plans were undertaken and hospital ambulances directory created.Results: The conference was finalized with no major incidents.The mass gathering preparedness team was available throughout the conference dates.Conclusion: Mass-gathering preparedness is a new concept for Ethiopian emergency care and should continue from this blueprint.Such preparedness should be continued for future mass-gathering events.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.064 | 0.015 |
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".