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Record W1494779747 · doi:10.26443/mjm.v9i1.744

Evidence-based Medicine and Rapid Response Team Implementation

2020· article· en· W1494779747 on OpenAlexvenueno aff
Jeffrey Bruckel

Bibliographic record

VenueMcGill Journal of Medicine · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

The implementation of Rapid Response Teams is becoming commonplace in U.S. hospitals, following the model developed in Australia. The Rapid Response Team is a method of bringing ICU-level patient care to the bedside of critically ill patients using a multidisciplinary team. Acute care unit staff are trained to recognize clinical deterioration using a set of vital sign calling criteria (systolic blood pressure below 90 mmHg, pulse below 60 or above 100, etc.). Many hospitals have been facing problems gaining needed support to make the organizational changes needed for the team to function properly. Some faculty physicians have expressed apprehension about losing control over their patients, and they have also highlighted the lack of rigorous experimental evidence that the teams work. Since there are so many confounding factors at work when trying to design an experimental study of this type of change, the study may not accurately portray the situation. Other evaluation methods should therefore be considered.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.118
metaresearch head score (Gemma)0.399
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.118
Threshold uncertainty score0.624

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1180.399
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0080.011
Bibliometrics0.0090.008
Science and technology studies0.0010.002
Scholarly communication0.0080.006
Open science0.0030.003
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0090.001

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 head0.868
GPT teacher head0.583
Teacher spread0.286 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations6
Published2020
Admission routes1
Has abstractyes

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