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Record W4200595020 · doi:10.1111/pan.14377

Risk assessment and optimization strategies to reduce perioperative respiratory adverse events in pediatric anesthesia—Part 1 patient and surgical factors

2021· review· en· W4200595020 on OpenAlexaff
Justin Hii, T. Wesley Templeton, David Sommerfield, Aine Sommerfield, Clyde Matava, Britta S. von Ungern‐Sternberg

Bibliographic record

VenuePediatric Anesthesia · 2021
Typereview
Languageen
FieldMedicine
TopicAirway Management and Intubation Techniques
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicinePerioperativeIntensive care medicineAdverse effectIncidence (geometry)Risk assessmentEmergency medicineAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Pediatric surgery cases are increasing worldwide. Within pediatric anesthesia, perioperative respiratory adverse events are the most common precipitant leading to serious complications. They can have intraoperative impact on the surgical procedure itself, lead to premature case termination and in addition may have postoperative impact resulting in longer hospitalization stays and costs. Although most perioperative respiratory adverse events can be promptly detected and managed, and will not lead to any sequelae, the risk of life-threatening progression remains. The incidence of respiratory adverse events increases in children with comorbid respiratory and/or nonrespiratory illnesses. Optimized perioperative patient care, risk-stratified care level choice, and practitioners with appropriate training allow for risk mitigation. This review will discuss patient and surgical risk factors with a focus on common patient comorbid illnesses and review scoring systems to quantify risk.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.033
GPT teacher head0.337
Teacher spread0.304 · 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

Citations45
Published2021
Admission routes1
Has abstractyes

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