MétaCan
Menu
Back to cohort

REDucing Unnecessary Coagulation Testing in the Emergency Department (REDUCED)

2017· article· en· W2610268389 on OpenAlexaff
Michael Fralick, Lisa K. Hicks, Hina Chaudhry, Nicola Goldberg, Alun Ackery, Rosane Nisenbaum, Michelle Sholzberg

Bibliographic record

VenueBMJ Quality Improvement Reports · 2017
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineEmergency departmentPartial thromboplastin timeProthrombin timeRate ratioCreatinineInternal medicineGastroenterologyEmergency medicineConfidence intervalCoagulation

Abstract

fetched live from OpenAlex

The PT/INR (prothrombin time/international normalized ratio) and aPTT (activated partial thromboplastin time) were tests developed in the early 20th century for specific and unique indications. Despite this, they are often ordered together routinely. The objective of this study was to determine if a multimodal intervention could reduce PT/INR and aPTT testing in the emergency department (ED). This was a prospective multi-pronged quality improvement study at St. Michael's Hospital. The initiative involved stakeholder engagement, uncoupling of PT/INR and aPTT testing, teaching, and most importantly a revision to the ED order panels. After changes to order panels, weekly rates of PT/INR and aPTT testing per 100 ED patients decreased (17.2 vs 38.4, rate ratio=0.45 (95% CI 0.43-0.47), p<0.001; 16.6 vs 37.8, rate ratio=0.44 (95% CI 0.42-0.46), p<0.001, respectively). Rate of creatinine testing per 100 ED patients, our internal control, increased during the same period (54.0 vs 49.7, rate ratio=1.09 (95% CI 1.06-1.12); p<0.0001) while the weekly rate per 100 ED patients receiving blood transfusions slightly decreased (0.5 vs 0.7, rate ratio=0.66 (95% CI 0.49-0.87), p=0.0034). We found that a simple process change to order panels was associated with meaningful reductions in coagulation testing without obvious adverse effects.

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)0.036
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0360.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.691
GPT teacher head0.606
Teacher spread0.085 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Quality Improvement ReportsSame topicHealthcare cost, quality, practicesFrench-language works237,207