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Record W2803593275 · doi:10.1177/1753495x18772993

Using anti-Xa level for adjusting intravenous unfractionated heparin infusion in peripartum thromboembolic disease

2018· article· en· W2803593275 on OpenAlexaff
Enrica Tse, Rshmi Khurana, Gwen Clarke, Winnie Sia

Bibliographic record

VenueObstetric Medicine · 2018
Typearticle
Languageen
FieldMedicine
TopicBlood Coagulation and Thrombosis Mechanisms
Canadian institutionsCanadian Blood ServicesUniversity of Alberta
Fundersnot available
KeywordsPartial thromboplastin timeHeparinMedicineDosingNomogramAnesthesiaAnticoagulantPregnancyThromboplastinThrombosisVenous thrombosisSurgeryInternal medicineCoagulation

Abstract

fetched live from OpenAlex

BACKGROUND: Intravenous unfractionated heparin infusion is often used to minimize the duration of time without anticoagulation around delivery in pregnant patients with high thrombotic risk. Activated partial thromboplastin time is commonly used to monitor and adjust heparin dose. However, using activated partial thromboplastin time is problematic in pregnancy because activated partial thromboplastin time response to unfractionated heparin is attenuated due to elevated Factor VIII levels and may lead to incorrect dosing. CASE: We report a case of deep venous thrombosis occurring in a term pregnancy managed by intravenous unfractionated heparin adjusted using anti-Xa level around the time of delivery. We modified the intravenous unfractionated heparin nomogram by using anti-Xa levels instead of activated partial thromboplastin time and observed lower dosing of unfractionated heparin than otherwise required to achieve and maintain target levels. CONCLUSION: This report demonstrates the feasibility and effectiveness of using anti-Xa level to monitor and adjust intravenous unfractionated heparin infusion in pregnancy.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.702
Threshold uncertainty score0.914

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.149
GPT teacher head0.362
Teacher spread0.213 · 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

Citations3
Published2018
Admission routes1
Has abstractyes

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