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Record W4409799575 · doi:10.29390/001c.136463

Roles of anesthesia assistants within the code blue team at in-hospital cardiopulmonary arrests: A retrospective analysis

2025· article· en· W4409799575 on OpenAlexaffvenue
Claire Ward, Melissa A. Berry, Maria Bou-Habib, Andrew D. Milne

Bibliographic record

VenueCanadian Journal of Respiratory Therapy · 2025
Typearticle
Languageen
FieldHealth Professions
TopicNursing Roles and Practices
Canadian institutionsDalhousie UniversityNova Scotia Health Authority
Fundersnot available
KeywordsMedicineAirway managementIntubationAnesthesiaRetrospective cohort studyMedical emergencySpecialtyVascular accessAirwayEmergency medicineRespiratory therapistIntensive care medicineSurgeryFamily medicine

Abstract

fetched live from OpenAlex

Introduction: Anesthesia assistants (AAs) are respiratory therapists or nurses who have additional sub-specialty training in the provision and maintenance of anesthesia. Their skill set includes advanced airway management, vascular access, and knowledge of vasoactive medications and resuscitative protocols. AAs function as non-physician members of the anesthesia team and can act as physician extenders to help offset the current shortage of anesthesiologists. Methods: This study was a retrospective analysis of AA roles at code blue events at an adult academic teaching centre. Data was extracted from administrative data collection forms completed by AAs at adult code blue events between 2017 and 2022. The data elements that each form captured included time and location of event, presumed cause of the code, airway management details, vascular access provision, medication preparation or administration, human factors and cognitive supports provided to the team by the AAs. Results: Administrative data collection forms from 320 code blue events were analyzed in this study. The most common primary causes of the code blue events were "arrest" (39%) and "respiratory failure" (26%). Regular floor beds (47%) and the intermediate care units (18%) were the most common locations of code blue events. Airway support was required in 77% of the codes, and in 50% of the cases requiring intubation, it was performed by the AAs. The first pass success rate for AA intubations was 83%, and overall success rate was 96%. In addition to airway management at codes, the AAs also reported providing numerous other valuable contributions to the team. The most reported supports provided to the team included cognitive support regarding resuscitation (49% of cases), intravenous access (19% of cases), and medication preparation or administration (9% of cases). Other roles included placement of arterial lines and drawing blood gases, obtaining interosseous vascular access, and assisting with patient transport to critical care units after resuscitation. Conclusions: Our study characterizes the supporting roles that AAs can provide as members of the code blue team and demonstrates their contributions to management of critically ill patients. The AAs' airway management, vascular access skillsets, and knowledge of vasoactive and resuscitative medications and protocols make them well-suited to the code blue team. In addition to assisting within the operating room team, AAs can also provide valuable support in non-operating room environments such as cardiopulmonary arrests.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.907

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.022
GPT teacher head0.346
Teacher spread0.324 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2025
Admission routes2
Has abstractyes

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