Getting Kids From the Big Easy Hospitals to Our Place (Not Easy): Preparing, Improvising, and Caring for Children During Mass Transport After a Disaster
Bibliographic record
Abstract
The incapacitating blows dealt to the New Orleans, Louisiana, health care infrastructure by Hurricane Katrina and its aftermath are unprecedented in the United States. Much can be learned about disaster preparedness from the events that unfolded in New Orleans after Katrina swept the Gulf shoreline. Problem areas in the management of this disaster such as internal and external communication failures, transportation, triage, personnel allocation, and resource allocation have long been identified in the literature as core disaster-response issues.1–3 In addition, damage to the physical plant, water and power system failures, and hazardous-materials exposure are encountered commonly in the face of disaster.1 Between 1950 and 2005, 286 hospital evacuations have been described, including horizontal evacuation (within the same floor), vertical evacuation (between floors), evacuation of a ward or wing, and complete hospital evacuation.4–10 Of the 286 between 1971 and 1999, 275 were reviewed by Sternberg et al.8 There are 22 reported cases of complete hospital evacuation since 1950; of these, 1 occurred in Canada and 8 resulted from the 1994 Northridge, California, earthquake.4,5,7–18 Of 43 reported incidents for which duration is known, only 12 evacuations lasted longer than 24 hours.8 Eleven percent of evacuations in the series reviewed by Sternberg et al listed at least 1 casualty, and the deadliest reported hospital disaster occurred in 1971, when partial collapse of a California Veteran's Administration hospital claimed 49 lives.8 Hurricanes are the third most common cause for hospital evacuation, accounting for 38 of 286 reported evacuations; only internal fire and internal hazardous-material events are more common. It is notable that only 3 evacuations since 1950 involved 1000 or more patients.8 Over a 5-day span in June 2001, Tropical Storm Allison brought nearly 39 inches … Address correspondence to Susan M. Distefano, MSN, RN, CNAA, BC. E-mail: smdistef{at}texaschildrenshospital.org
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".