Is it taking longer to die in paediatric intensive care in England and Wales?
Bibliographic record
Abstract
INTRODUCTION: All-cause infant and childhood mortality has decreased in the UK over the last 30 years. Advances in paediatric critical care have increased survival in paediatric intensive care units (PICUs) but may have affected how and when children die in PICU. We explored factors affecting length of stay (LOS) of children who died in PICU over an 11-year period. METHODS: We analysed demographic and clinical data of 165 473 admissions to PICUs in England and Wales, from January 2003 to December 2013. We assessed time trends in LOS for survivors and non-survivors and explored the effect of demographic and clinical characteristics on LOS for non-survivors. RESULTS: LOS increased 0.310 days per year in non-survivors (95% CI 0.169 to 0.449) and 0.064 days per year in survivors (95% CI 0.046 to 0.083). The proportion of early deaths (<24 h of admission) fell 0.44% points per year (95% CI -0.971 to 0.094), but the proportion of late deaths (>28 days of PICU stay) increased by 0.44% points per year (95% CI 0.185 to 0.691). The paediatric index of mortality score in early deaths increased by 0.77% points per year (95% CI 0.31% to 1.23%). DISCUSSION: Increased LOS in children who die in PICU is driven by a decreased proportion of early deaths and an increased proportion of late deaths. This trend, combined with an increase in the severity of illness in early deaths, is consistent with a reduction in early mortality for acutely ill children, but a prolongation of life for those children admitted to PICU with life-limiting illnesses.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".