Decision Analysis in SHared decision making for Thromboprophylaxis during Pregnancy (DASH-TOP): a sequential explanatory mixed-methods pilot study
Bibliographic record
Abstract
OBJECTIVES: To gain insight into formal methods of integrating patient preferences and clinical evidence to inform treatment decisions, we explored patients' experience with a personalised decision analysis intervention, for prophylactic low-molecular-weight heparin (LMWH) in the antenatal period. DESIGN: Mixed-methods explanatory sequential pilot study. SETTING: Hospitals in Canada (n=1) and Spain (n=4 sites). Due to the COVID-19 pandemic, we conducted part of the study virtually. PARTICIPANTS: 15 individuals with a prior venous thromboembolism who were pregnant or planning pregnancy and had been referred for counselling regarding LMWH. INTERVENTION: A shared decision-making intervention that included three components: (1) direct choice exercise; (2) preference elicitation exercises and (3) personalised decision analysis. MAIN OUTCOME MEASURES: Participants completed a self-administered questionnaire to evaluate decision quality (decisional conflict, self-efficacy and satisfaction). Semistructured interviews were then conducted to explore their experience and perceptions of the decision-making process. RESULTS: Participants in the study appreciated the opportunity to use an evidence-based decision support tool that considered their personal values and preferences and reported feeling more prepared for their consultation. However, there were mixed reactions to the standard gamble and personalised treatment recommendation. Some participants could not understand how to complete the standard gamble exercises, and others highlighted the need for more informative ways of presenting results of the decision analysis. CONCLUSION: Our results highlight the challenges and opportunities for those who wish to incorporate decision analysis to support shared decision-making for clinical decisions.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.048 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".