A call to action: attention to paediatric-specific disaster preparedness
Bibliographic record
Abstract
Natural and man-made disasters have been increasing worldwide. Six hundred natural disasters were documented worldwide in 2016 compared with 200 in 1980.1 The Global Terrorism Index score (which reflects the relative impact of terrorism incidents annually) increased ninefold from 2000 to 2015.2 With this increasing frequency of mass-casualty events globally, hospitals and their healthcare professionals (HCPs) must be ready to receive and manage a large influx of patients. Additionally, these institutions and front-line healthcare workers must be prepared to care for disaster victims of all ages, including the paediatric population, regardless of the hospitals’ typical intake patterns. In order to be best prepared, many countries across the world have developed healthcare-related disaster plans. Similar to other countries, France has developed the Organisation de la Reponse du Systeme de Sante en Situations Sanitaires Exceptionnelles plan to guide the management of mass casualties. However, this specific plan lacks explicit guidelines with respect to the management of paediatric victims; an oversight that is commonly seen in disaster plans worldwide. Children have unique anatomical, physiological and psychological aspects that increase their risk in the case of disasters.3 Consequently, paediatric disaster victims require an assessment and treatment plan different than that for adults. Therefore, there is a need, on a global level, …
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.029 | 0.037 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".