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Record W2140562914 · doi:10.1177/1076029611405186

Venous Thromboembolism Pharmacy Intervention Management Program With an Active, Multifaceted Approach Reduces Preventable Venous Thromboembolism and Increases Appropriate Prophylaxis

2011· article· en· W2140562914 on OpenAlexaff
Charles E. Mahan, Mohamed Hussein, Alpesh Amin, Alex C. Spyropoulos

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2011
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversityHamilton General Hospital
FundersSanofi
KeywordsVenous thromboembolismMedicinePharmacyIntervention (counseling)Intensive care medicineInternal medicineFamily medicineThrombosisNursing

Abstract

fetched live from OpenAlex

Two concepts relating to venous thromboembolism (VTE) prevention have recently emerged-"appropriate" prophylaxis and "preventable" VTE. We evaluated whether a human alert, as part of a pharmacy intervention program, can increase appropriate prophylaxis and decrease preventable symptomatic VTE in hospitalized patients. This prospective study with retrospective data collection was conducted utilizing data from 1879 patients in 2006 as a control cohort. The intervention cohort data were from 1646 patients during 2007, after program implementation. The rate of appropriate prophylaxis increased from 23.8% in 2006 to 37.9% in 2007 (odds ratio 1.8; 95% confidence interval [CI] = 1.6-2.1; P < .0001). Preventable VTE incidence was reduced by 74% (95% CI = 44%-88%) from 18.6 to 4.9 per 1000 patient discharges in 2006 and 2007, respectively (P = .0006). In conclusion, a pharmacy-led multifaceted intervention can significantly increase the rates of appropriate prophylaxis and significantly reduce the incidence of preventable VTE in hospitalized 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.913
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0000.001
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 head0.072
GPT teacher head0.350
Teacher spread0.277 · 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 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

Citations23
Published2011
Admission routes1
Has abstractyes

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