Evacuation of Intensive Care Units During Disaster: Learning From the Hurricane Sandy Experience
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
OBJECTIVE: Data on best practices for evacuating an intensive care unit (ICU) during a disaster are limited. The impact of Hurricane Sandy on New York City area hospitals provided a unique opportunity to learn from the experience of ICU providers about their preparedness, perspective, roles, and activities. METHODS: We conducted a cross-sectional survey of nurses, respiratory therapists, and physicians who played direct roles during the Hurricane Sandy ICU evacuations. RESULTS: Sixty-eight health care professionals from 4 evacuating hospitals completed surveys (35% ICU nurses, 21% respiratory therapists, 25% physicians-in-training, and 13% attending physicians). Only 21% had participated in an ICU evacuation drill in the past 2 years and 28% had prior training or real-life experience. Processes were inconsistent for patient prioritization, tracking, transport medications, and transport care. Respondents identified communication (43%) as the key barrier to effective evacuation. The equipment considered most helpful included flashlights (24%), transport sleds (21%), and oxygen tanks and respiratory therapy supplies (19%). An evacuation wish list included walkie-talkies/phones (26%), lighting/electricity (18%), flashlights (10%), and portable ventilators and suction (16%). CONCLUSIONS: ICU providers who evacuated critically ill patients during Hurricane Sandy had little prior knowledge of evacuation processes or vertical evacuation experience. The weakest links in the patient evacuation process were communication and the availability of practical tools. Incorporating ICU providers into hospital evacuation planning and training, developing standard evacuation communication processes and tools, and collecting a uniform dataset among all evacuating hospitals could better inform critical care evacuation in the future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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