Update on the Coordinated Efforts of Looking After the Health Care Needs of Children and Young People Fleeing the Conflict Zone of Ukraine Presenting to European Emergency Departments—A Joint Statement of the European Society for Emergency Paediatrics and the European Academy of Paediatrics
Bibliographic record
Abstract
This joint statement by the European Society for Emergency Paediatrics and European Academy of Paediatrics aims to highlight recommendations for dealing with refugee children and young people fleeing the Ukrainian war when presenting to emergency departments (EDs) across Europe. Children and young people might present, sometimes unaccompanied, with either ongoing complex health needs or illnesses, mental health issues, and injuries related to the war itself and the flight from it. Obstacles to providing urgent and emergency care include lack of clinical guidelines, language barriers, and lack of insight in previous medical history. Children with complex health needs are at high risk for complications and their continued access to specialist healthcare should be prioritized in resettlements programs. Ukraine has one of the lowest vaccination coverages in the Europe, and outbreaks of cholera, measles, diphtheria, poliomyelitis, and COVID-19 should be anticipated. In Ukraine, rates of multidrug resistant tuberculosis are high, making screening for this important. Urgent and emergency care facilities should also prepare for dealing with children with war-related injuries and mental health issues. Ukrainian refugee children and young people should be included in local educational systems and social activities at the earliest opportunity.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".