Personalising haemophilia management with shared decision making
Bibliographic record
Abstract
Abstract The current standard of care for treating people with haemophilia (PWH) in the developed world is prophylaxis with regular infusions of clotting factor concentrates. Gene therapy is being investigated as a new treatment paradigm for haemophilia and if approved would potentially eliminate the need for chronic, burdensome infusions. In recent years, shared decision making (SDM) has become increasingly common in patient care settings. SDM is a stepwise process that relies on reciprocal information sharing between the practitioner and patient, resulting in health care decisions stemming from the informed preferences of both parties. SDM represents a departure from the traditional, paternalistic clinical model where the practitioner drives the treatment decision and the patient passively defers to this decision. As the potential introduction of gene therapy in haemophilia may transform the current standard of care, and impact disease management and goals in unique ways, both practitioners and PWH may find their knowledge tested when considering the appropriate use of a novel technology. Therefore, it is incumbent upon haemophilia practitioners to foster an open, trusting, and supportive relationship with their patients, while PWH and their caregivers must be knowledgeable and feel empowered to participate in the decision making process to achieve truly shared treatment decisions.
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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 | 0.036 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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, 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".