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Record W2889186395 · doi:10.4037/ajcc2018114

Improving Communication Between Surgery and Critical Care Teams: Beyond the Handover

2018· article· en· W2889186395 on OpenAlexaff
Christian Turner, Barbara Haas, Christie Lee, Savtaj S. Brar, Michael E. Detsky, Laveena Munshi

Bibliographic record

VenueAmerican Journal of Critical Care · 2018
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsSinai Health System
Fundersnot available
KeywordsMedicineChecklistIntensive care unitPatient safetyIntervention (counseling)HandoverEmergency medicineMedical emergencyHealth careNursingIntensive care medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Structured communication tools for postoperative surgical handover to the intensive care unit (ICU) have shown promise, yet little work has addressed ongoing daily communication between the surgery and ICU teams thereafter. OBJECTIVES: Evaluation of a novel, 2-part communication intervention between surgery and ICU teams focused on postoperative handover and ongoing daily communication. METHODS: A mixed-methods, pre- and postintervention survey study was conducted in a closed quaternary medical-surgical ICU. Study participants (N = 112) included ICU physicians, nurses, allied health professionals, and physicians on the surgical team. The intervention consisted of a handover checklist completed postoperatively on arrival in the ICU and a 5-item communication tool completed daily by the surgical team. RESULTS: = .008). No significant improvement was seen in communication regarding disposition or overall improvement in patient safety risk from communication errors. CONCLUSIONS: A simple handover checklist improved health care practitioner satisfaction with communication during postoperative handover to the ICU. Concise daily communication tools are an appropriate option for improving ongoing communication between surgeons and the ICU team thereafter.

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)0.004
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 score0.441
Threshold uncertainty score0.749

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.015
GPT teacher head0.334
Teacher spread0.319 · 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

Citations9
Published2018
Admission routes1
Has abstractyes

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