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Record W2130467730 · doi:10.1160/th05-01-0064

Bridging anticoagulation with low-molecular-weight heparin after interruption of warfarin therapy is associated with a residual anticoagulant effect prior to surgery

2005· article· en· W2130467730 on OpenAlexaff
Karen Woods, Gary Foster, Mark Crowther, James D. Douketis

Bibliographic record

VenueThrombosis and Haemostasis · 2005
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsWarfarinBridging (networking)HeparinMedicineAnticoagulantThrombosisLow molecular weight heparinAnticoagulant therapySurgeryInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

Bridging anticoagulation with low-molecular-weight heparin (LMWH) is common in patients who require temporary interruption of warfarin before surgery or a procedure, but whether such patients have a residual anticoagulant effect just before a procedure is not known. Consecutive patients who received bridging anticoagulation with LMWH had anti-Xa levels measured just before a procedure. The proportion of patients with a residual anticoagulant effect, defined as an anti-Xa level > or = 0.10 IU/ml, was determined. Multivariable regression analysis identified predictors of a residual anticoagulant effect, expressed as an odds ratio (OR) and corresponding 95% confidence interval (CI). A pre-procedure residual anticoagulant effect was detected in 12 of 73 (16%) patients overall, in 11 of 37 (30%) patients who received therapeutic-dose LMWH, and in 1 of 36 patients (3%) who received low-dose LMWH. Receiving therapeutic-dose LMWH (OR = 118.8; 95% CI: 5.8, 999.9), and increasing age (OR = 4.0; 95% CI: 1.3, 12.5) were predictors of a residual pre-procedure anticoagulant effect. In patients who require bridging anticoagulation with LMWH, a residual anticoagulant effect from LMWH is detected in 1 of 6 patients, and receiving therapeutic-dose LMWH is the strongest predictor of such an effect.

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 score0.275
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.035
GPT teacher head0.306
Teacher spread0.271 · 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

Citations68
Published2005
Admission routes1
Has abstractyes

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