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Record W4387872219 · doi:10.1080/20479700.2023.2266637

ICU patient flow: To premature step-down or not? A simulation analysis

2023· article· en· W4387872219 on OpenAlexaff
Yawo Mamoua Kobara, Felipe F. Rodrigues, Camila P. E. de Souza, David A. Stanford

Bibliographic record

VenueInternational Journal of Healthcare Management · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsWestern UniversityUniversity of Windsor
Fundersnot available
KeywordsProxy (statistics)MedicineDownstream (manufacturing)Intensive care unitMedical emergencyUpstream (networking)Intensive care medicineOperations managementComputer scienceEconomicsComputer network

Abstract

fetched live from OpenAlex

A Step-Down Unit (SDU) provides an intermediate Level of Care for patients from an Intensive Care Unit (ICU) as their condition becomes less acute. SDU congestion and upstream patient arrivals force ICU administrators to incur costs, either in the form of overstays or premature step-downs. Based on a proxy for patient acuity level called the ‘Nine Equivalents of Nursing Manpower Score (NEMS)’, patients were classified into high-acuity and low-acuity. Two patient flow policies were developed and simulated: one allowing for premature step-down actions when the system is congested and the other allowing for patient rejection actions when the system is congested. The results show that the patient-rejection policy has a net health service benefit that significantly exceeds the premature step-down policy. Based on these results, it can be concluded that premature step-down contributes to congestion downstream. Counter-intuitively, premature step-down should therefore be discouraged and patient diversion actions should be further explored as viable options for congested ICUs.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.621
Threshold uncertainty score0.591

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
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 head0.067
GPT teacher head0.465
Teacher spread0.398 · 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 designSimulation or modeling
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

Citations1
Published2023
Admission routes1
Has abstractyes

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