Using the Emergency Care Data Set for the epidemiological surveillance of Children and Young People aged less than 18 years: a case study of COVID-19 in England 2020-2023
Bibliographic record
Abstract
Abstract Background The Emergency Care Data Set provides insight into emergency care activity in England, and combined with COVID-19 surveillance data, can provide new insights into acute COVID-19 infection. Methods This study identified individuals <18 years old who tested positive for SARS-CoV-2 between February 2020 and March 2023 and attended emergency care 1-14 days after a positive test. The study’s main objective was to explore ED attendance outcomes by demographic characteristics. Results There were significant differences (p < 0.05) across most of the characteristics of <18s admitted to hospital from emergency departments, and those who were discharged from ED. <18s in IMD decile 1 (14.9%) made up the highest proportion of admissions, with those in less deprived areas having a greater proportion of individuals discharged from ED. February to August 2020 (1.5%) and September 2022 to March 2023 (2.8%) saw the highest proportion of <18 cases attending ED, though the highest number of cases were seen between September 2021 and February 2022. Conclusions There is great value in the use of ECDS. It facilitates quick, regular insights into the health outcomes of key demographics, and provides a window into the health-seeking behaviours of individuals. Furthermore, outcomes of emergency care attendance can potentially inform assessments of infection severity across multiple demographics during outbreaks and pandemics.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".