Values and preferences towards the use of prophylactic low-molecular-weight heparin during pregnancy: a convergent mixed-methods secondary analysis of data from the decision analysis in shared decision making for thromboprophylaxis during pregnancy (DASH-TOP) study
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) in pregnancy is a major cause of maternal morbidity and mortality, and the use of preventive low-molecular-weight heparin (LMWH) can be challenging. Clinical guidelines recommend eliciting pregnant individuals' preferences towards the use of daily injections of LMWH and discussing the best option through a shared decision-making (SDM) approach. Our aim was to identify individuals' preferences concerning each of the main clinical outcomes, and categorize attributes influencing the use of LMWH during pregnancy. METHODS: Design: Convergent mixed-methods. PARTICIPANTS: Pregnant women or those planning a pregnancy with VTE recurrence risk. INTERVENTION: A SDM intervention about thromboprophylaxis with LMWH in pregnancy. ANALYSIS: Quantitatively, we report preference scores assigned to each of the health states. Qualitatively, we categorized preference attributes using Burke's pentad of motives framework: act (what needs to be done), scene (patient's context), agent (perspectives and influence of people involved in the decision), agency (aspects of the medication), and purpose (patient's goals). We use mixed-method convergent analysis to report findings using side-by-side comparison of concordance/discordance. RESULTS: We comprehensively determined preferences for using LMWH by pregnant individuals at risk of VTE: through value elicitation exercises we found that the least valued health state was to experience a pulmonary embolism (PE), followed by major obstetrical bleeding (MOB), deep vein thrombosis (DVT), and using daily injections of LMWH (valued as closest to a 'healthy pregnancy'); through interviews we found that: previous experiences, access to care (scene) and shared decision-making (agent) affected preferences. LMWH's benefits were noted, but substantial drawbacks were described (agency). For participants, the main goal of using LMWH was avoiding any risks in pregnancy (purpose). Side-by-side comparisons revealed concordance and discordance between health states and motives. CONCLUSIONS: Mixed-methods provide a nuanced understanding of LMWH preferences, by quantifying health states preferences and exploring attributes qualitatively. Incorporating both methods may improve patient-centered care around preference-sensitive decisions in thromboprophylaxis during pregnancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".