MétaCan
Menu
Back to cohort
Record W2889519877 · doi:10.22374/cjgim.v13i3.270

Patients’ Satisfaction with Anticoagulant Treatment for Venous Thromboembolism

2018· article· en· W2889519877 on OpenAlexaffvenue
Roxanne Dault, Lucie Blais, Alain Vanasse, Paul Farand, Geneviève Letemplier, Marie-France Beauchesne

Bibliographic record

VenueCanadian Journal of General Internal Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversité de MontréalHôpital du Sacré-Cœur de MontréalCentre Hospitalier Universitaire de SherbrookeUniversité de Sherbrooke
Fundersnot available
KeywordsMedicineAnticoagulant therapyVenous thromboembolismObservational studyAnticoagulantMedical prescriptionPatient satisfactionLow molecular weight heparinVitamin kIntensive care medicineInternal medicineHeparinSurgeryThrombosisPharmacology

Abstract

fetched live from OpenAlex

Background Data on treatment expectation and perception towards vitamin K antagonists (VKAs), direct oral anticoagulants (DOACs), and low molecular weight heparins (LMWH) for the management of venous thromboembolism (VTE) are sparse. Methods Prospective observational study including subjects admitted to the hospital with a diagnosis of VTE and a prescription of VKA, DOAC, or a LMWH. Treatment expectations, convenience and satisfaction were assessed using the Perception of anticoagulant treatment questionnaire (PACT-Q) at baseline and at three months. Results A total of 140 patients were included. Treatment expectations regarding ease of use and the ability to self-manage anticoagulation therapy were higher in patients on DOACs. However, overall treatment satisfaction scores were similar at three months between the groups. Conclusion Patients with VTE who are prescribed an anticoagulant have different expectations at baseline but appear to have similar treatment satisfaction regardless of the type of anticoagulant prescribed.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.322
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.026
GPT teacher head0.289
Teacher spread0.263 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of General Internal MedicineSame topicVenous Thromboembolism Diagnosis and ManagementFrench-language works237,207