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Record W2319857486 · doi:10.1097/jnn.0b013e3181b6beae

Nurse Practitioner-Based Sign-Out System to Facilitate Patient Communication on a Neurosurgical Service

2009· article· en· W2319857486 on OpenAlexaffabout
Deborah L Rabinovitch, Melinda Hamill, Clauda Zanchetta, Mark Bernstein

Bibliographic record

VenueJournal of Neuroscience Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineService (business)Medical emergencyNurse practitionersHouse staffTest (biology)NursingHealth careFamily medicine

Abstract

fetched live from OpenAlex

Failure to communicate important patient information between physicians causes medical errors and adverse patient events. On-call neurosurgery physicians at the Toronto Western Hospital do not know the medical details of all the patients that they are covering at night because they do not care for the entire service of patients during the day. Because there is no formal handover system to transfer patient information to the on-call physician, a nurse practitioner-based sign-out system was recently introduced. Its effectiveness for communication was evaluated with preintervention-postintervention questionnaires and by recording daily logins. There was a statistically significant decrease in number of logins after 8 weeks of use (p = .05, Fisher's exact test), and the tool was abandoned after 16 weeks. Modifications identified to improve the system include the ability to sort by attending physician and to automatically populate the list with new patients. Effective communication is important for reducing medical errors, and perhaps these modifications will facilitate this important endeavor.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.804
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.055
GPT teacher head0.334
Teacher spread0.280 · 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

Citations11
Published2009
Admission routes2
Has abstractyes

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