Decision Analysis in SHared decision making for Thromboprophylaxis during Pregnancy (DASH-TOP): a sequential explanatory mixed methods pilot study protocol
Bibliographic record
Abstract
INTRODUCTION: Decision analysis is a quantitative approach to decision making that could bridge the gap between decisions based solely on evidence and the unique values and preferences of individual patients, a feature especially important when existing evidence cannot support clear recommendations and there is a close balance between harms and benefits for the treatments options under consideration. Low molecular weight heparin (LMWH) for the prevention of venous thromboembolism (VTE) during pregnancy represents one such situation. The objective of this paper is to describe the rationale and methodology of a pilot study that will explore the application of decision analysis to a shared decision-making process involving prophylactic LMWH for pregnant women or those considering pregnancy who have experienced a VTE. METHODS AND ANALYSIS: We will conduct an international, mixed methods, explanatory, sequential study, including quantitative data collection and analysis followed by qualitative data collection and analysis. In step I, we will ask women who are pregnant or considering pregnancy and have experienced VTE to participate in a shared decision-making intervention for prophylactic LMWH. The intervention consists of three components: a direct choice exercise, a values elicitation exercise and a personalised decision analysis. After administration of the intervention, we will ask women to make a treatment decision and measure decisional conflict, self-efficacy and satisfaction. In step II, which follows the analysis of quantitative data, we will use the results to inform the qualitative interview. Step III will be a qualitative descriptive study that explores participants' experiences and perceptions of the intervention. In step IV, we will integrate findings from the qualitative and quantitative analyses to obtain meta-inferences. ETHICS AND DISSEMINATION: Site-specific ethics boards have approved the study. All participants will provide informed consent. The research team will take an integrated approach to knowledge translation.
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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 | 0.079 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.050 | 0.006 |
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".