MétaCan
Menu
Back to cohort
Record W4319295135 · doi:10.1097/aco.0000000000001246

Beyond mortality: definitions and benchmarks of outcome standards in paediatric anaesthesiology

2023· review· en· W4319295135 on OpenAlexaff
Vanessa A. Olbrecht, Thomas Engelhardt, Joseph D. Tobias

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2023
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsMedicineOutcome (game theory)Intensive care medicineMEDLINE

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.212
GPT teacher head0.446
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueCurrent Opinion in AnaesthesiologySame topicCardiac, Anesthesia and Surgical OutcomesFrench-language works237,207