Beyond mortality: definitions and benchmarks of outcome standards in paediatric anaesthesiology
Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this study was to review the evolution of safety and outcomes in paediatric anaesthesia, identify gaps in quality and how these gaps may influence outcomes, and to propose a plan to address these challenges through the creation of universal outcome standards and a paediatric anaesthesia designation programme. RECENT FINDINGS: Tremendous advancements in the quality and safety of paediatric anaesthesia care have occurred since the 1950 s, resulting in a near absence of documented mortality in children undergoing general anaesthesia. However, the majority of data we have on paediatric anaesthesia outcomes come from specialized academic institutions, whereas most children are being anaesthetized outside of free-standing children's hospitals. SUMMARY: Although the literature supports dramatic improvements in patient safety during anaesthesia, there are still gaps, particularly in where a child receives anaesthesia care and in quality outcomes beyond mortality. Our goal is to increase equity in care, create standardized outcome measures in paediatric anaesthesia and build a verification system to ensure that these targets are accomplished. The time has come to benchmark paediatric anaesthesia care and increase quality received by all children with universal measures that go beyond simply mortality.
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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 | 0.042 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| 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".