Decontamination of Mass Casualties — Re-evaluating Existing Dogma
Bibliographic record
Abstract
The events of 11 September 2001 became the catalyst for many to shift their disaster preparedness efforts towards mass-casualty incidents. Emergency responders, healthcare workers, emergency managers, and public health officials worldwide are being tasked to improve their readiness by acquiring equipment, providing training and implementing policy, especially in the area of mass-casualty decontamination. Accomplishing each of these tasks requires good information, which is lacking. Management of the incident scene and the approach to victim care varies throughout the world and is based more on dogma than scientific data. In order to plan effectively for and to manage a chemical, mass-casualty event, we must critically assess the criteria upon which we base our response. This paper reviews current standards surrounding the response to a release of hazardous materials that results in massive numbers of exposed human survivors. In addition, a significant effort is made to prepare an international perspective on this response. Preparations for the 24-hour threat of exposure of a community to hazardous material are a community responsibility for first-responders and the hospital. Preparations for a mass-casualty event related to a terrorist attack are a governmental responsibility. Reshaping response protocols and decontamination needs on the differences between vapor and liquid chemical threats can enable local responders to effectively manage a chemical attack resulting in mass casualties. Ensuring that hospitals have adequate resources and training to mount an effective decontamination response in a rapid manner is essential.
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.
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 | 0.055 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".