142 Values and preferences towards the use of prophylactic low-molecular- weight heparin during pregnancy: a convergent mixed-methods analysis of data from the decision analysis in shared decision making for thromboprophylaxis during pregnancy (DASH-TOP) study
Bibliographic record
Abstract
Introduction Venous thromboembolism (VTE) 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 LMWH and discussing the best option through a shared decision-making (SDM) approach. This study identifies identified individuals’ preferences concerning main health state, and categorizes attributes influencing the use of LMWH. Methods Design Convergent mixed-methods. Participants Pregnant women or those planning a pregnancy with VTE recurrence risk. Intervention: A SDM intervention about LMWH thromboprophylaxis 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: scene, agent, agency, act, and purpose. We use mixed-method convergent analysis to report findings using side-by-side comparison. Results 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’). Women’s previous experiences, access to care (scene) and shared decision-making (agent) affected preferences. LMWH’s benefits were noted, but substantial drawbacks were described (agency). The main goal was avoiding the risk of VTE (purpose). Side-by-side comparisons revealed concordance between motives and DVT and PE health states. Discordance appeared between using daily injections of LMWH and agent- and agency motives and between MOB and agency motives. Discussion Mixed-methods provide a nuanced understanding of LMWH preferences in pregnancy, by quantifying health states preferences and exploring attributes qualitatively. Incorporating both methods may improve patient-centered care. Conclusion Convergent mixed-method analysis helps to intuitively ‘tell the whole story’ of patient’s needs, desires, and values, ultimately facilitating discussions between patients and clinicians, promoting a SDM process, and leading patients to make the right decision that fits in their life.
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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 | 0.035 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".