An Adaptation of the PICE System as a Template for Disaster Planning: An Exercise in Facilitating Integrative Consultation
Bibliographic record
Abstract
Field Exercise for Mass Casualty Care underwent a complete metamorphosis after the Great Hanshin-Awaji Earthquake in 1995.Before that time, the exercise was stripped of all of its contents with an attitude that a large disaster had no bearing on Japan.It showed a marked tendency toward the annual exercise held in an emergency day of September, especially carried out disaster drills organized by local governments and evacuation exercises in the use of medical facilities.After the bitter experiences of the earthquake, the Government felt the necessity of preparedness for disasters, and set out to reform the preparedness system under the leadership of the Ministries of Health and Welfare (MHW), and Home Affairs.The MHW nominated six base hospitals in prefectures, and gave each the role of the center hospitals for emergency medicine.Therefore, medical education and training to the disasters has been held in prefectures and is progressing practically in the contents of a drill.Recently, education and training in Japan has become proficient on mass casualty care, and many improvements over the international level.In the future, nurseries of specialists in advanced manageable medical administration in disaster relief operations and the diffusion of training into the civil defense are necessary.
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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 | 0.023 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.010 |
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".