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Record W2511352278 · doi:10.1097/jpn.0000000000000193

The Impact of Standardized Acuity Assessment and a Fast-Track on Length of Stay in Obstetric Triage

2016· article· en· W2511352278 on OpenAlexaff
David S. Smithson, Rachel Twohey, Nancy Watts, Robert Gratton

Bibliographic record

VenueThe Journal of Perinatal & Neonatal Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsTriageInterquartile rangeMedicineFast trackVisual acuityOphthalmologyEmergency medicineInternal medicineSurgery

Abstract

fetched live from OpenAlex

To prospectively assess the impact of a standardized 5-category Obstetrical Triage Acuity Scale (OTAS) and a fast-track for lower-acuity patients on patient flow. Length of stay (LOS) data of women presenting to obstetric triage were abstracted from the electronic medical record prior to (July 1, 2011, to March 30, 2012) and following OTAS implementation (April 1 to December 31, 2012). Following computerized simulation modeling, a fast-track for lower acuity women was implemented (January 1, 2013, to February 28, 2014). Prior to OTAS implementation (8085 visits), the median LOS was 105 (interquartile range [IQR] = 52-178) minutes. Following OTAS implementation (8131 visits), the median LOS decreased to 101 (IQR = 49-175) minutes (P = .04). The LOS did not correlate well with acuity. Simulation modeling predicted that a fast-track for OTAS 4 and 5 patients would reduce the LOS. The LOS for lower-acuity patients in the fast-track decreased to 73 (IQR = 40-140) minutes (P = .005). In addition, the overall LOS (12 576 visits) decreased to 98 (IQR = 47-172) minutes (6.9% reduction; P < .001). Standardized assessment of acuity and a fast-track for lower acuity pregnant women decreased the overall LOS and the LOS of lower-acuity patients.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.915
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.365
Teacher spread0.347 · 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 designOther design
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

Citations10
Published2016
Admission routes1
Has abstractyes

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