Increase in emergency admissions to hospital for children aged under 15 in England, 1999-2010: national database analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate a reported rise in the emergency hospital admission of children in England for conditions usually managed in the community. SETTING AND DESIGN: Population-based study of hospital admission rates for children aged under 15, based on analysis of Hospital Episode Statistics and population estimates for England, 1999-2010. MAIN OUTCOME: Trends in rates of emergency admission to hospital. RESULTS: The emergency admission rate for children aged under 15 in England has increased by 28% in the past decade, from 63 per 1000 population in 1999 to 81 per 1000 in 2010. A persistent year-on-year increase is apparent from 2003 onwards. A small decline in the rates of admissions lasting 1 day or more has been offset by a twofold increase in short-term admissions of <1 day. Considering the specific conditions where high emergency admission rates are thought to be inversely related to primary care quality, admission rates for upper respiratory tract infections rose by 22%, lower respiratory tract infections by 40%, urinary tract infections by 43% and gastroenteritis by 31%, while admission rates for chronic conditions fell by 5.6%. CONCLUSIONS: The continuing increase in very-short-term admission of children with common infections suggests a systematic failure, both in primary care (by general practice, out-of-hours care and National Health Service Direct) and in hospital (by emergency departments and paediatricians), in the assessment of children with acute illness that could be managed in the community. Solving the problem is likely to require restructuring of the way acute paediatric care is delivered.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".