Blackout 2003: Preparedness and Lessons Learned from the Perspectives of Four Hospitals
Bibliographic record
Abstract
INTRODUCTION: The blackout in North America of August 2003 was one of the worst on record. It affected eight US states and parts of Canada for >24 hours. Additionally, two large US cities, Detroit, Michigan and Cleveland, Ohio, suffered from a loss of water pressure and a subsequent ban on the use of public supplies of potable water that lasted four days. A literature review revealed a paucity of literature that describes blackouts and how they may affect the medical community. METHODS: This paper includes a review of after-action reports from four inner-city, urban hospitals supplemented accounts from the authors' hospital's emergency operations center (EOC). RESULTS: Some of the problems encountered, included: (1)lighting; (2) elevator operations; (3) supplies of water; (4) communication operations; (5) computer failure; (6) lack of adequate supplies of food; (7) mobility to obtain X-ray studies; (8) heating, air condition, and ventilation; (9) staffing; (10) pharmacy; (11) registration of patients; (12) hospital EOC; (13) loss of isolation facilities; (14) inadequate supplies of paper; (15) impaired ability to provide care for non-emergency patients; (16) sanitation; and (17) inadequate emergency power. DISCUSSION: The blackout of 2003 uncovered problems within the US hospital system, ranging from staffing to generator coverage. This report is a review of the effects that the blackout and water ban of 2003 had on hospitals in a large inner-city area. Also discussed are solutions utilized at the time and recommendations for the future. CONCLUSION: The blackout of 2003 was an excellent test of disaster/emergency planning, and produced many valuable lessons to be used in future events.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.009 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.006 | 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".