Disaster preparedness in French paediatric hospitals 2 years after terrorist attacks of 2015
Bibliographic record
Abstract
OBJECTIVE: We aimed to determine paediatric hospital preparedness for a mass casualty disaster involving children in both prehospital and hospital settings. The study findings will serve to generate recommendations, guidelines and training objectives. DESIGN AND SETTING: The AMAVI-PED study is a cross-sectional survey. An electronic questionnaire was sent to French physicians with key roles in specialised paediatric acute care. RESULTS: In total, 81% (26 of 32) of French University Hospitals were represented in the study. A disaster plan AMAVI with a specific paediatric emphasis was established in all the paediatric centres. In case of a mass casualty event, paediatric victims would be initially admitted to the paediatric emergency department for most centres (n=21; 75%). Paediatric anaesthesiologists, paediatric surgeons and paediatric radiologists were in-house in 20 (71%), 5 (18%) and 12 (43%) centres, respectively. Twenty-three (82%) hospitals had a paediatric specialised mobile intensive care unit and seven (25%) of these could provide a prehospital emergency response. Didactic teaching and simulation exercises were implemented in 20 (71%) and 22 (79%) centres, respectively. Overall, physician participants rated the level of readiness of their hospital as 6 (IQR: 5-7) on a 10-point readiness scale. CONCLUSION: Paediatric preparedness is very heterogeneous between the centres. Based on the study findings, we suggest that a national programme must be defined and guidelines generated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".