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Record W2049666571 · doi:10.1177/107602960501100102

Low-Molecular-Weight Heparin for the Treatment of Venous Thromboembolism in the Elderly

2005· review· en· W2049666571 on OpenAlexaff
Graham F. Pineo, Russell D. Hull

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2005
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineWarfarinPulmonary embolismLow molecular weight heparinHeparinVenous thromboembolismIntensive care medicineVenous thrombosisDeep veinThrombosisAnticoagulantPopulationIncidence (geometry)SurgeryInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

Venous thromboembolism (deep vein thrombosis and/or pulmonary embolism) is a common problem in the elderly population. Indeed, increasing age is a significant risk factor for venous thromboembolism. The treatment of venous thromboembolism in the elderly population presents certain unique problems related to aging, such as decreasing body weight, increasing renal insufficiency and numerous comorbid conditions, which complicate therapy. Treatment of venous thromboembolism in the elderly has been complicated by an increased incidence of bleeding, particularly with the use of warfarin. The risk of bleeding may be substantially reduced by carefully adjusting the warfarin dose to maintain a therapeutic INR and for this purpose anticoagulant management clinics have been shown to be useful. The low-molecular-weight heparins have been shown to be efficacious and safe for the treatment of venous thromboembolism in several clinical trials, including many patients in the older age brackets. Furthermore, these agents can safely be used in the out-of-hospital setting. Long-term use of low-molecular-weight heparin is an alternative to the use of oral anticoagulant therapy, particularly in patients with cancer or recurrent venous thromboembolism.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.109
GPT teacher head0.415
Teacher spread0.306 · 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 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

Citations7
Published2005
Admission routes1
Has abstractyes

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