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Record W2395684743 · doi:10.1097/aco.0000000000000206

Anaesthesia outside of the operating room

2015· review· en· W2395684743 on OpenAlexaff
Katherine Taylor, Peter C. Laussen

Bibliographic record

VenueCurrent Opinion in Anaesthesiology · 2015
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineCardiac catheterizationAdverse effectPatient safetyIntensive care medicineMedical emergencySurgeryHealth careInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The focus of cardiac catheterization has changed from principally a diagnostic procedure to providing therapeutic options at various stages of childhood and adult congenital heart disease. The paediatric cardiac catheterization laboratory functions as a 'satellite' operating room. Combined ('hybrid') procedures with interventional cardiologists and cardiac surgeons present additional challenges for anaesthesia. The increased patient and procedure complexity represents higher risk for anaesthesia-related adverse events. RECENT FINDINGS: This review concentrates on the recent efforts to determine these patient and procedure-related risks. Multicentre registries have been developed, generating information regarding adverse events and patient outcomes. Standardized adverse events ratios allow comparisons between institutions and providers. Models to identify high-risk groups have been developed. SUMMARY: Advances in paediatric cardiac catheterization have created significant challenges for delivering anaesthesia in this environment. Anaesthetists need to have an integral role in the cardiac catheterization team, understanding and anticipating the risks for patients and leading the organization of workflow. Techniques used to improve systems in the operating room have been introduced to the cardiac catheterization laboratory to promote patient safety.

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)0.001
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 score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.001
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 head0.151
GPT teacher head0.422
Teacher spread0.271 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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