Developing a two‐sided intervention to facilitate shared decision‐making in haemophilia: decision boxes for clinicians and patient decision aids for patients
Bibliographic record
Abstract
BACKGROUND: People with haemophilia face many treatment decisions, which are largely informed by evidence from observational studies. Without evidence-based 'best' treatment options, patient preferences play a large role in decisions regarding therapy. The shared decision-making (SDM) process allows patients and health care providers to make decisions collaboratively based on available evidence, and patient preferences. Decision tools can help the SDM process. The objective of this project was to develop two-sided decision tools, decision boxes for physicians and patient decision aids for patients, to facilitate SDM for treatment decisions in haemophilia. METHODS: Development of the decision tools comprised three phases: topic selection, prototype development and usability testing with targeted end-users. Topics were selected using a Delphi survey. Tool prototypes were based on a previously validated framework and were informed by systematic literature reviews. Patients, through focus groups, and physicians, through interviews, reviewed the prototypes iteratively for comprehensibility and usability. RESULTS: The chosen topics were: (i) prophylactic treatment: when to start and dosing, (ii) choosing factor source and (iii) immunotolerance induction: when to start and dosing. Intended end users (both health care providers and haemophilia patients and caregivers) were engaged in the development process. Overall perception of the decision tools was positive, and the purpose of using the tools was well received. CONCLUSIONS: This study demonstrates the feasibility of developing decision tools for haemophilia treatment decisions. It also provides anecdotal evidence of positive perceptions of such tools. Future directions include assessment of the tools' practical value and impact on clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".