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Record W2312269846 · doi:10.1097/ncq.0000000000000093

Surgical Suite to Pediatric Intensive Care Unit Handover Protocol

2014· article· en· W2312269846 on OpenAlexaff
Tracie Northway, Gordon Krahn, Kristine Thibault, L. Yarske, Nataliya Yuskiv, Niranjan Kissoon, Jean‐Paul Collet

Bibliographic record

VenueJournal of Nursing Care Quality · 2014
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsSuiteHandoverProtocol (science)Intensive care unitIntensive careProcess (computing)Medical emergencyMedicineComputer sciencePediatric intensive care unitNursingIntensive care medicineOperating system

Abstract

fetched live from OpenAlex

The article reports the long-term sustainability of a standardized transfer protocol from cardiac surgical suite to the pediatric intensive care unit. Using rapid process improvement technique, the original mean defect rate per handover decreased from 13.2 to 0 and 0.3, 12, and 24 months postimplementation, respectively. This study stresses the importance of long-term assessment to control for possible observation biases; it also illustrates a successful implementation strategy that used video recording to engage staff in identifying solutions to the observed defects.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.168
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.062
GPT teacher head0.450
Teacher spread0.388 · 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

Citations22
Published2014
Admission routes1
Has abstractyes

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