The Canadian Armed Forces medical response to Typhoon Haiyan
Bibliographic record
Abstract
BACKGROUND: In the setting of international disaster response, an important challenge is determining when it is appropriate to withdraw deployed assets as the acute disaster response transitions to recovery and rebuilding. We describe our experience with realtime data collection during our medical response to Typhoon Haiyan as a means to guide military aid mission parameters. METHODS: The operational medical headquarters prospectively developed a database for use in this mission. Mobile medical teams (MMTs) were deployed to provide primary care, and the nurse designated to each MMT was responsible for entering and transmitting data daily to the medical headquarters. Data collected included the MMT location, basic patient demographics, the primary reason for the encounter and any treatment provided. These encounters were then classified as disaster, acute or chronic. RESULTS: Between Nov. 16 and Dec. 16, 2013, medical care was provided to 6596 local nationals; 238 (3.6%) had disaster-related illness or injury, 4321 (65.5%) had acute postdisaster medical conditions and 2037 (30.9%) sought medical care for chronic conditions. Of the 257 patients with traumatic injuries, 28 (11%) had disaster-related injuries and 214 (83%) had acute injuries that occurred postdisaster. CONCLUSION: The data collected during the mission to the Phillippines was compiled with performance metrics from the other Disaster Assistance Response Team components to help advise the Canadian government regarding mission duration. We recommended that data collection continue on all future missions and be modified to provide further information to larger disaster coordination teams, such as the United Nations Office for the Coordination of Humanitarian Affairs.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".