Shared decision‐making related to treatment of haemophilia: A scoping review of influential factors and available support tools
Bibliographic record
Abstract
INTRODUCTION: Treatment selection in haemophilia is increasingly challenging given evolving therapeutic options and the need for individualization. Shared decision-making (SDM) approaches have recently gained interest, though a synthesis of available studies is lacking. AIM: A scoping review was conducted to summarize literature reporting on factors impacting treatment SDM in haemophilia and tools or models available to support such decisions. METHODS: PubMed, Embase, the Cochrane Library, Web of Science and grey literature were searched for studies published through August 2023. Original studies reporting on facilitators and barriers to haemophilia SDM and SDM tools were included and analyzed for themes, characteristics and gaps. RESULTS: A total of 625 records were identified and 14 unique studies were selected (factors influencing treatment SDM, n = 7; SDM tools, n = 7). The studies typically included input from persons with haemophilia, caregivers and healthcare practitioners (HCPs). Thematic organization of factors influencing SDM revealed three main categories: knowledge, patient characteristics and HCP-patient interactions. Availability of information was a commonly reported facilitator of SDM, while poor HCP-patient engagement was a commonly reported barrier. Tools varied in focus, with some facilitating general treatment SDM while others supported selection of certain therapy types. The studies underscored additional factors critical for SDM, such as alignment of HCP-patient perceptions, shared language and tailoring of tools to specific subpopulations. CONCLUSION: Few studies report on treatment SDM factors and tools in haemophilia; available tools vary considerably. It remains unclear whether published tools have been successfully implemented into clinical practice. Additional research is warranted.
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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 | 0.043 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.019 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".