MétaCan
Menu
Back to cohort
Record W3172001116 · doi:10.1136/bmjoq-2020-001123

Reducing emergency department visits in patients with deep vein thrombosis: introducing a standardised outpatient treatment pathway

2021· article· en· W3172001116 on OpenAlexaff
Tony Wan, Anna Rahmani, Michaela Hanáková, Hing Yi Wong, Glenyth Caragata, Emily Ross, Oluwadamilola Akinyemi

Bibliographic record

VenueBMJ Open Quality · 2021
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsSimon Fraser UniversityVancouver Coastal HealthProvidence Health CareUniversity of British Columbia
Fundersnot available
KeywordsMedicineEmergency departmentDeep veinOvercrowdingEmergency medicineOutpatient clinicGuidelineThrombosisMedical emergencySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Deep vein thrombosis (DVT) is an acute medical condition that requires urgent diagnosis and treatment to prevent significant morbidity and mortality. Patients with DVT frequently present to the emergency department (ED) because the necessary diagnostic investigations and medical treatment for successful outpatient management are not readily accessible in the outpatient clinics. A collaborative quality improvement project was undertaken to implement and evaluate a standardised outpatient treatment pathway designed to direct patients with a newly diagnosed DVT from the ultrasound department to the thrombosis clinic, where guideline-based management for DVT can be accomplished without ED visits. During the baseline period (1 February 2017 to 31 January 2019), the number of ED visits for DVT was 383 with an average of 16 visits per month. During the intervention period (1 February 2019 to 31 January 2020), the number of ED visits for DVT was 106 with an average of 8.8 visits per month. This represents almost a 50% reduction in the average ED visits during the intervention period. A standardised outpatient treatment pathway can significantly reduce the number of ED visits in patients with DVT, potentially improving patient care and reducing ED overcrowding.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.145
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0010.000

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 head0.051
GPT teacher head0.379
Teacher spread0.328 · 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.

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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Open QualitySame topicVenous Thromboembolism Diagnosis and ManagementFrench-language works237,207