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Use of decision aids for shared decision making in venous thromboembolism: A systematic review

2016· review· en· W2375687962 on OpenAlexaff
Geoffrey D. Barnes, Brett Izzo, Marisa Conte, Vineet Chopra, Anne Holbrook, Angela Fagerlin

Bibliographic record

VenueThrombosis Research · 2016
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineDecision aidsVenous thromboembolismPulmonary embolismIntensive care medicineMEDLINESurgeryThrombosisAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Optimal care of patients with venous thromboembolism requires the input of patient preferences into clinical decision-making. However, the availability and impact of decision aids to facilitate shared decision making in care of venous thromboembolism is not well known. OBJECTIVES: To assess the availability, clinical impact and outcomes associated with the use of decision aids in patients with or at risk for venous thromboembolism. PATIENTS/METHODS: A systematic review of the literature was performed exploring the use of decision aids in patients with venous thromboembolism. Criteria for primary inclusion required use of patient values clarification in the decision aid. A secondary review without the requirement of a patient values clarification was performed to be more inclusive. The data was summarized such that knowledge gaps and opportunities for enquiry were identified. RESULTS: The primary review identified one study that explored the decision to extend anticoagulation in patients with a recent venous thromboembolism beyond the stipulated 3-month duration. The secondary review identified an additional study exploring the decision to undergo computer tomography testing in patients at low risk for pulmonary embolism in an emergency department setting. Both studies were of modest quality given a lack of control group for comparison analysis. CONCLUSIONS: Despite numerous calls to increase use of shared decision-making, a paucity of data exists to help patients engage in the treatment decisions for venous thromboembolism. Future studies of additional VTE clinical decisions with longer-term clinical outcomes appear necessary.

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)0.008
metaresearch head score (Gemma)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.369
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0140.002
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.292
GPT teacher head0.508
Teacher spread0.216 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations12
Published2016
Admission routes1
Has abstractno

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