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Record W2057104175 · doi:10.1055/s-2001-14669

Treatment of Deep Vein Thrombosis

2001· review· en· W2057104175 on OpenAlexaff
Françis Couturaud, Clive Kearon

Bibliographic record

VenueSeminars in Vascular Medicine · 2001
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsJuravinski HospitalHamilton General Hospital
Fundersnot available
KeywordsMedicineThrombosisDeep veinSurgeryHeparinAnticoagulantRisk factorVenous thrombosisLow molecular weight heparinAnticoagulant therapyInternal medicine

Abstract

fetched live from OpenAlex

Most patients who present with deep vein thrombosis (DVT) can be treated with weight-adjusted, fixed-dose, low molecular heparin as an outpatient. The subsequent duration of oral anticoagulant therapy should be individualized according to the risk of recurrent venous thromboembolism and the risk of anticoagulant-induced bleeding. The risk of recurrence is low if thrombosis was provoked by a major reversible risk factor such as surgery; 3 months of treatment is usually adequate for such patients. The risk of recurrence is high if thrombosis was unprovoked ("idiopathic") or associated with a nonreversible risk factor such as active cancer; at least 6 months, and sometimes indefinite, anticoagulant therapy is indicated for such patients. The presence of an antiphospholipid antibody, and other selected thrombophilic states, favors more prolonged therapy within each of the categories noted previously. Systemic thrombolytic therapy helps to restore venous patency and probably reduces the risk of the postthrombotic syndrome; however, it is associated with an unacceptable risk of bleeding. Catheter-directed thromboylsis, particularly for isolated iliofemoral thrombosis, may be beneficial and needs further evaluation in controlled trials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.356
Teacher spread0.309 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations11
Published2001
Admission routes1
Has abstractyes

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