Impact of Deferring Critically Ill Children Away from Their Designated Paediatric Critical Care Unit: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The impact of deferring critically ill children in referral hospitals away from their designated pediatric critical care unit (PCCU) on patients and the healthcare system is unknown. We aimed to identify factors associated with deferrals and patient outcomes and to study the impact of a referral policy implemented to balance PCCU bed capacity with regional needs. METHODS: We conducted a population-based retrospective cohort study of admissions to a PCCU following inter-facility transport from 2004 to 2016 in Ontario, Canada. RESULTS: Of 10,639 inter-facility transfers, 24.8% (95% confidence interval [CI]: 23.5-26.1%) were deferred during pre-implementation and 16.0% (95% CI: 15.1-16.9%) during post-implementation of a referral policy. Several factors, including previous intensive care unit admissions, residence location, presenting hospital factors, patient co-morbidities, specific designated PCCUs and winter (versus summer) season, were associated with deferral status. Deferrals were not associated with increased mortality. CONCLUSIONS: Deferral from a designated PCCU does not confer an increased risk of death. Implementation of a referral policy was associated with a consistent referral pattern in 84% of transfers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".