Risk factors for unplanned paediatric intensive care unit admission after anaesthesia—an international multicentre study
Bibliographic record
Abstract
Objectives: Unplanned intensive care unit (ICU) admissions are associated with near-miss events, morbidity, and mortality. We describe the rate, resource utilization, and outcomes of paediatric patients urgently admitted directly to ICU post-anaesthesia compared to other sources of unplanned ICU admissions. Methods: We performed a secondary analysis of data from specialist paediatric hospitals in 7 countries. Patients urgently admitted to the ICU post-anaesthesia were combined and matched with 1 to 3 unique controls from unplanned ICU admissions from other locations by age and hospital. Demographic, clinical, and outcome variables were compared using the Wilcoxon rank-sum test for continuous variables and chi-square or Fisher's exact test for categorical variables. The effect of admission sources on binary outcomes was estimated using univariable conditional logistic regression models with stratification by matched set of anaesthesia and non-anaesthesia admission sources. Results: Most admissions were <1 year of age and for respiratory reasons. Admissions post-anaesthesia were shorter, occurred later in the day, and were more likely to be mechanically ventilated. Admissions post-anaesthesia were less likely to have had a previous ICU admission (4.8% compared to 11%, P=0.032) or PIM 'high-risk diagnosis' (9.5% versus 17.2%, P=0.035) but there was no difference in the number of subsequent ICU admissions. There was no difference in the PIM severity of illness score and no mortality difference between the groups. Conclusions: Young children and respiratory indications dominated unplanned ICU admissions post-anaesthesia, which was more likely later in the day and with mechanical ventilation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".