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Making Decisions about Thromboprophylaxis in Pregnancy: Women's Values and Preferences

2014· article· en· W2555822990 on OpenAlexaff
Shannon M. Bates, Pablo Alonso‐Coello, Mark H. Eckman, Kari A.O. Tikkinen, Shanil Ebrahim, Luciane Cruz Lopes, Sarah D. McDonald, Ignacio Neumann, Yuqing Zhang, Anne Flem Jacobsen, Elie A. Akl, Amparo Santamaría, Gordon Guyatt

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsHospital for Sick ChildrenMcMaster UniversityThrombosis and Atherosclerosis Research Institute
Fundersnot available
KeywordsMedicinePregnancyLow molecular weight heparinObstetricsAbsolute risk reductionRisk assessmentVenous thromboembolismThrombosisConfidence intervalSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: The risk of pregnancy-related venous thromboembolism (VTE) is increased in women with a history of thrombosis. Although antepartum low molecular weight heparin (LMWH) prophylaxis can reduce this risk; the baseline risk of recurrence and the absolute magnitude of the risk reduction with prophylaxis are uncertain. Further, LMWH prophylaxis is costly, burdensome, medicalizes pregnancy, and may increase the risk of bleeding. Therefore, uncertainty persists regarding the net benefit of thromboprophylaxis and recommendations about the use of antepartum LMWH should be sensitive to pregnant women’s values and preferences, which have not previously been studied. Methods: We undertook an international multicenter cross-sectional interview study that included women with a history of VTE who were pregnant, planning pregnancy, or might consider pregnancy in the future. Women were classified as high (5 to 10%) or low (1 to 5%) risk of recurrent antepartum VTE. We ascertained willingness to receive LMWH during pregnancy through direct choice exercises involving real-life scenarios using the participant’s estimated VTE (high or low) and bleeding risks, hypothetical scenarios (low, medium and high risk of recurrence) and a probability trade-off exercise. Study outcomes included the minimum absolute reduction in VTE risk at which women changed from declining to accepting LMWH, along with possible determinants of this threshold, and participant choice of management strategy in her real-life and the three hypothetical scenarios. Results: 123 women from seven centers in six countries participated. Using a fixed 16% VTE risk without prophylaxis, the mean threshold reduction in risk at which women were willing to use LMWH was 4.3% (95% CI, 3.5 – 5.1%). Pregnant women and those planning a pregnancy (compared to those who might consider pregnancy in the future) and those with less than 2 weeks of experience with using LMWH during pregnancy (compared to those with more experience) required a greater risk reduction to use prophylaxis. In the real life scenario, there was there a significant difference in the proportion of women choosing prophylaxis between those at high risk (87.1%) and low risk (60.0%) of recurrence (p=0.01). The proportion of women choosing to use LMWH prophylaxis was 65.1% for the low risk hypothetical scenario (4% risk of recurrence), 79.7% for the medium risk scenario (10% risk of recurrence) and 87.8% for the high risk scenario (16% risk of recurrence). Conclusions: Most women with prior VTE will choose prophylaxis during a subsequent pregnancy, regardless of whether they are categorized as high or low risk of recurrence. Nevertheless, 40% of lower risk women will decline LMWH, as will over 10% of high risk women. Thus, these results mandate individualized clinical decision-making for women considering LMWH use during pregnancy, and weak guideline recommendations for LMWH use that highlight the need for individualized decision-making. Disclosures No relevant conflicts of interest to declare.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.290
Teacher spread0.261 · 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 designQualitative
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

Citations0
Published2014
Admission routes1
Has abstractyes

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