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Record W2128974009 · doi:10.4997/jrcpe.2011.404

Evaluation of the use of venous thromboembolism prophylaxis in hospitalised medical patients

2011· article· en· W2128974009 on OpenAlexaffabout
Mohamed Panju, D. Mattace Raso, Akshay J. Patel, Akbar Panju, J Ginsberg

Bibliographic record

VenueThe Journal of the Royal College of Physicians of Edinburgh · 2011
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University Medical CentreJuravinski Hospital
Fundersnot available
KeywordsMedicineContraindicationPulmonary embolismDeep veinVenous thromboembolismIntensive care medicineThrombosisEmergency medicineInternal medicineAlternative medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Venous thromboembolism (VTE) prophylaxis is effective in the prevention of deep vein thrombosis (DVT) and pulmonary embolism (PE) in medical patients. METHODS: A retrospective chart review was performed on medical inpatients at two academic hospitals in Hamilton, Ontario to investigate if patients received VTE prophylaxis as per current guidelines. RESULTS: An analysis was performed on 762 patient charts and 170 met inclusion criteria for use of pharmacological VTE prophylaxis. Of these, 91 (54%) received pharmacological VTE prophylaxis. In 63 patients with a contraindication to pharmacological VTE prophylaxis, 16 (25%) received non-pharmacological VTE prophylaxis. CONCLUSION: The provision rate of pharmacological VTE prophylaxis in hospitalised medical patients who met pre-defined clinical criteria for prophylaxis was 54%. The rate of prophylaxis increased with additional VTE risk factors to a peak rate of 67%. There is room for the development of strategies to improve the use of VTE prophylaxis in hospitalised medical 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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.732
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 science0.0010.000
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 head0.047
GPT teacher head0.261
Teacher spread0.214 · 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

Citations8
Published2011
Admission routes2
Has abstractyes

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