Developing a Two‐Sided Decision Box to Facilitate Shared Decision‐Making for Switching From Conventional to Pharmacokinetic‐Tailored Prophylaxis in Haemophilia
Bibliographic record
Abstract
INTRODUCTION: Decision-making in haemophilia is challenging due to the small evidence base, disease heterogeneity, and inter-patient variability. Shared decision-making (SDM) supports patient-clinician decisions. AIM: Creation of a two-sided decision box facilitating SDM for haemophilia patients switching from conventional (weight-based) to pharmacokinetic driven individualized prophylaxis. METHODS: We developed an SDM tool as suggested by Giguere et al. A stakeholder discussion with haemophilia treaters and patients identified goals, burden, values and preferences. Benefits and harms of key questions were described with a common metric and base. A systematic review identified relevant evidence. PubMed, Medline, Embase, CINAHL, Cochrane Reviews and Cochrane Trials were searched from inception to June 2022. Original articles reporting switches from conventional to individualized prophylaxis within the same product class were included. Evidence from the review and discussion guided the design of the decision box. Feedback informed multiple iterations before the final version. RESULTS: A total of 569 titles and abstracts were screened, yielding 88 full texts. Eight studies met inclusion criteria: six reported on bleeding rates, four on dosing interval, three on factor consumption, three on quality of life, two on adherence, and two on costs. One study recommended SDM for tailored prophylaxis. Discussions unanimously suggested decision aids to facilitate the choice to switch to tailored prophylaxis. Clinicians highlighted the need for evidence on treatment individualization, while patients valued viewing relevant examples. CONCLUSION: Creating decision tools for haemophilia is challenging due to low quantity and quality of evidence. Our decision box is ready for use with careful application of clinical judgement.
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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 | 0.200 | 0.298 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".