The Canadian Armed Forces medical response to Typhoon Haiyan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it