Dental Participants in Mass Disasters—A Retrospective Study with Future Implications
Bibliographic record
Abstract
Mass casualty incidents continue to require the services of forensic dentists to determine the identity of victims. Across North America and Europe. teams of forensic dentists train, using mock disaster exercises, to prepare for such duties. It is vital that these mock exercises simulate the features of real disaster situations as far as possible. In order to inform those responsible for the design and implementation of mock exercises, a study was undertaken to determine the features of actual disasters that dental personnel had attended. Using a questionnaire, data were solicited from 38 odontologists. The average number of disasters attended by the respondents was eight, with an average casualty number of 94. Aircraft crashes were the most frequent cause of disasters that were attended by the odontologists. The authors conclude that future mock disaster exercises should replicate features of aircraft crashes as closely as possible by using commingled, fragmented, and burned remains. In addition, mock disasters should require the identification of a realistic number of individuals to ensure authenticity and the maximum logistical preparedness of participants.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".