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Record W4412410363 · doi:10.1186/s12873-025-01283-z

Length of stay in the emergency department and its associated input-, throughput-, and output factors at two hospitals in Sweden

2025· article· en· W4412410363 on OpenAlexaff
Jonas Andersson, Lisa Kurland, Lena Nordgren, Annelie K. Gusdal, Ivy Cheng

Bibliographic record

VenueBMC Emergency Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsSunnybrook Health Science Centre
FundersÖrebro Universitet
KeywordsMedicineEmergency departmentThroughputEmergency medicineMedical emergencyMEDLINENursingTelecommunications

Abstract

fetched live from OpenAlex

BACKGROUND: Prolonged emergency department length of stay (EDLOS) is a worldwide issue associated with increased mortality, decreased patient satisfaction and poor quality of care. The factors influencing EDLOS have not been comprehensively studied in the context of Swedish EDs. This study’s objective is to determine the input-, throughput- and output factors associated with EDLOS, at two urban EDs in Sweden. METHODS: Data was collected from two hospitals. All patient visits during the two-year study period were included. Patients who left without being seen by a physician were excluded. The explanatory factors included patient characteristics, medical data, and hospital bed occupancy data. Multi-variable linear regression analysis was used to test the associations between the factors and EDLOS. RESULTS: The top contributors to prolonged EDLOS were diagnostic imaging, which added between 64 and 149 min of EDLOS, diagnostic testing at central laboratory (53–99 min), followed by intra-ED zone transfer (46–94 min). Arriving during crowding or being admitted during high hospital bed occupancy had a significant but relatively small absolute effect on the outcome. CONCLUSIONS: Throughput factors had far greater impact on EDLOS than both input- and output factors. Adapting strategies to the structural and procedural characteristics of each setting may enhance the effectiveness of improvement efforts. CLINICAL TRIAL NUMBER: Not applicable.

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)0.002
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 score0.020
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.359
Teacher spread0.309 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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