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Record W2167134605 · doi:10.1182/blood-2002-03-0965

How we manage venous thromboembolism during pregnancy

2002· review· en· W2167134605 on OpenAlexafffund
Shannon M. Bates, Jeffrey S. Ginsberg

Bibliographic record

VenueBlood · 2002
Typereview
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health ResearchHeart and Stroke Foundation of Canada
KeywordsMedicinePregnancyWarfarinVenous thromboembolismLow molecular weight heparinHeparinChildbirthIntensive care medicineThrombosisAnticoagulantDosingObstetricsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

During pregnancy, physiologic and anatomic changes can complicate the diagnosis of venous thromboembolism (VTE) as well as the management of patients with a high risk of or established VTE. As in nonpregnant subjects, clinical diagnosis of VTE by itself is unreliable and accurate objective testing is essential. Few diagnostic studies of VTE have been performed in pregnant women and, therefore, approaches are largely extrapolated from those used in nonpregnant subjects with modifications to limit the radiation exposure and overcome the limitations of diagnostic testing in pregnancy. Therapy of established VTE during pregnancy consists of therapeutic doses of unfractionated heparin (UFH) or low-molecular-weight heparin (LMWH), generally given throughout pregnancy subcutaneously and for 4 to 6 weeks after childbirth. A key unresolved issue includes the optimum dosing of LMWH therapy. Maternal warfarin can be safely used after childbirth because it is safe to use in the breast-fed infant of a mother receiving warfarin. Finally, pregnant women with prior VTE (with or without a hypercoagulable state) have an increased risk of recurrent venous thrombosis. A recent study has demonstrated that for women with a single episode of prior VTE, many can be managed without anticoagulants. However, for many, anticoagulant therapy with prophylactic UFH or LMWH is a reasonable option.

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 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 score0.898
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.045
GPT teacher head0.286
Teacher spread0.242 · 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

Citations125
Published2002
Admission routes2
Has abstractyes

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