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Record W3009422255 · doi:10.20452/pamw.15225

Treatment of venous thromboembolism in elderly patients in the era of direct oral anticoagulants

2020· article· en· W3009422255 on OpenAlexaff
Tobias Tritschler, Lana A. Castellucci, Nick van Es, Drahomir Aujesky, Grégoire Le Gal

Bibliographic record

VenuePolskie Archiwum Medycyny Wewnętrznej · 2020
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsVenous thromboembolismMedicineIntensive care medicineInternal medicineThrombosis

Abstract

fetched live from OpenAlex

The incidence of venous thromboembolism (VTE) and VTE‑related morbidity and mortality increase with advancing age. Over the past decade, substantial advances in the treatment of VTE have been achieved. Most notably, direct oral anticoagulants (DOACs) were introduced, which offer simple treatment regimens across a broad spectrum of patients with VTE and have become the first‑choice anticoagulants in many individuals in this population. Even though elderly patients are underrepresented in clinical trials, the extrapolation of overall study results to the elderly subpopulation can be considered justified regarding acute VTE treatment and the choice of anticoagulant agent. In the elderly, DOACs are not only associated with a lower bleeding risk but they also appear to be even more efficacious than vitamin K antagonists in preventing recurrent VTE during the acute treatment period. Determining the optimal treatment duration is the most challenging aspect of VTE management in elderly patients. The risk of bleeding increases with advancing age, and several risk factors for recurrent VTE after stopping anticoagulation are also more frequent in the elderly. Clinical decision rules estimating the risk of recurrent VTE and bleeding have limited utility in elderly patients. Shared decision making considering patients' preferences and values is therefore crucial to help determine individual treatment duration in these 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)0.000
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 score0.412
Threshold uncertainty score0.887

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.032
GPT teacher head0.291
Teacher spread0.259 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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