The GOAL‐Hēm journey: Shared decision making and patient‐centred outcomes
Bibliographic record
Abstract
INTRODUCTION: GOAL-Hēm is a novel, haemophilia-specific, patient-centred outcome measure (PCOM) based on goal attainment scaling, allowing people with haemophilia (PwH) to set and monitor the attainment of individualized goals for treatment. AIM: To provide a thorough overview of the creation, validation, and development of GOAL-Hēm. METHODS: Clinician workshops were held to develop a haemophilia-specific goal menu. Qualitative data from semistructured interviews with PwH and their caregivers guided further revisions to the goal menu (i.e., goal domains and descriptors). A feasibility study was performed including a 12-week, prospective, noninterventional evaluation involving clinicians and PwH at four US haemophilia treatment centres. Finally, the Patient Voice Study gathered feedback from PwH and their caregivers via an online survey, interviews, and a focus group. RESULTS: The feasibility study validated GOAL-Hēm with successful outcomes in construct/content validity and responsiveness, including a large effect in patient- and clinician-rated goal attainments. The Patient Voice Study led to significant refinement of GOAL-Hēm goals and descriptors, resulting in a more straightforward and relatable menu for PwH and their caregivers. Overall, GOAL-Hēm captured qualitative data in areas important to PwH and employed quantitative methods to evaluate meaningful changes in those areas. The individualized tool was well equipped to handle the complex and chronic nature of haemophilia and was endorsed by PwH, their caregivers, and clinicians. CONCLUSION: The GOAL-Hēm development journey may serve as a roadmap for other PCOMs in a variety of settings, including clinical studies, haemophilia treatment centres for care planning, and as a tool to gather real-world evidence.
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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 | 0.035 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".