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Record W4415226079 · doi:10.1016/j.ccrj.2025.100127

Rinaldo’s role in the medical emergency team and rapid response systems

2025· article· en· W4415226079 on OpenAlexaff
Daryl Jones, Donna Goldsmith, Michael A. DeVita, Ken Hillman AO

Bibliographic record

VenueCritical Care and Resuscitation · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsColumbia College
Fundersnot available
KeywordsRapid response teamPromulgationIntensive care unitHealth careEmergency departmentUnit (ring theory)Project commissioning

Abstract

fetched live from OpenAlex

In the 1990s, there was emerging evidence that patients admitted to hospitals frequently suffered in-hospital cardiac arrest, unplanned admission to the intensive care unit (ICU), and potentially preventable in-hospital death. These events were often preceded by objective signs of instability and suboptimal recognition and response by hospital ward staff. Rinaldo Bellomo collaborated with key Australian and international leaders to develop a novel and paradigm-shifting model of care referred to as the medical emergency team (MET). This team is comprised of senior staff members who are experts in the assessment and management of acutely deteriorating patients. In Australia and New Zealand, staff members from the ICU are frequently the team leaders for the MET. The team is called when a patient develops objective signs of clinical deterioration, prior to the onset of cardiac arrest. Rinaldo led, mentored, and supervised a systematic and structured research program that evaluated the nature and effectiveness of the MET at Austin Health and throughout Australia. This commenced with single-centre before-and-after studies and progressed to the first Australian ICU cluster-randomised controlled trial. His unique skillset was pivotal in the emergence and promulgation of this model of care worldwide resulting in countless lives saved from preventable morbidity and 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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.442
Teacher spread0.403 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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