(P1-8) The Port-Au-Prince 2010 Earthquake - Unique Lessons Learned by Florida One Disaster Medical Assistance Team (FL1 DMAT) in the First International Dmat Deployment
Bibliographic record
Abstract
Introduction The devastating Haiti earthquake of January 10, 2010 resulted in 250,000 dead, more than 300,000 wounded and at least 1.3 million displaced. As the poorest nation in the Western Hemisphere, life in Haiti was already fraught with poverty and one of the highest HIV rates in the world. After the earthquake, life in Haiti became intolerable. As Chief Medical Officer of Florida One DMAT, the author helped to coordinate medical relief operations at the US Embassy in Port-au-Prince beginning within 60 hours of the earthquake. The author and his FL1 DMAT team supported medical relief operations not only at the US Embassy but also at the Toussaint L'Ouverture International Airport for air evacuation of survivors to Miami and at Terminal Varreux for coordination of ingress/egress casualty operations for the USNS Comfort hospital ship. Results Unique lessons were learned in this first ever deployment of US DMATs on foreign soil. The presentation will describe the medical operations, the triage process, the challenges of operating on foreign soil, and the results of the relief efforts. Recommendations will be offered to facilitate future international DMAT deployments including development of Standard Operating Procedures (SOPs) for DMAT international deployments and increased coordination between the US Department of State (who have jurisdiction over US assets on foreign soil) and the US Department of Health & Human Services (who are the coordinating governmental department for DMAT operations).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.007 |
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".