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Low‐molecular‐weight heparin or warfarin for anticoagulation in pregnant women with mechanical heart valves: what are the risks? A retrospective observational study

2012· article· en· W1850537665 on OpenAlexaff
S. Basude, Cindy Hein, SL Curtis, Amanda Clark, Johanna Trinder

Bibliographic record

VenueBJOG An International Journal of Obstetrics & Gynaecology · 2012
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Issues in Pregnancy
Canadian institutionsSt. Michael's Hospital
FundersUniversity of Bristol
KeywordsWarfarinMedicineLow molecular weight heparinHeparinThrombosisRetrospective cohort studyObservational studyPregnancyObstetricsAdverse effectMechanical heartSurgeryInternal medicineAtrial fibrillation

Abstract

fetched live from OpenAlex

Please cite this paper as: Basude S, Hein C, Curtis S, Clark A, Trinder J. Low‐molecular‐weight heparin or warfarin for anticoagulation in pregnant women with mechanical heart valves: what are the risks? A retrospective observational study. BJOG 2012;119:1008–1013. The management of anticoagulation in pregnant women with mechanical heart valves is complex. The maternal and fetal outcomes of 32 pregnancies in 15 women on three different anticoagulation regimens were compared. Anticoagulation with low‐molecular‐weight heparin (n = 4), warfarin (n = 22) and combination therapy (n = 6) resulted in adverse maternal events in four (100%), three (50%) and three (14%) women, and resulted in fetal losses in one (25%), 17(77%) and three (50%) pregnancies, respectively. Whereas the rate of fetal loss in the warfarin group was high, all women in the LMWH and half of those in the combination group had serious adverse maternal events, including valve thrombosis, maternal death and postpartum haemorrhage.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.363
Teacher spread0.287 · 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 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

Citations76
Published2012
Admission routes1
Has abstractyes

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