Quantifying the optimal factor VIII levels to achieve patient-centric and clinician-relevant outcomes among people with hemophilia A: a SHELF elicitation study
Bibliographic record
Abstract
BACKGROUND: Hemophilia A is an inherited bleeding disorder caused by a deficiency of clotting factor VIII (FVIII), leading to joint bleeding and arthropathy. While prophylactic FVIII therapy reduces bleeding, evidence suggests maintaining higher FVIII levels (FL) may better protect joint health, particularly in physically active individuals and those with joint damage. However, data on optimal FLs required to prevent joint deterioration and complications remains limited. RESEARCH DESIGN AND METHODS: This study utilized the Sheffield Elicitation Framework (SHELF) methodology to elicit expert opinions on optimal FLs for patient-centric and clinical outcomes. Five European hemophilia experts participated in virtual workshops, providing probability-based estimates of FLs required to prevent bleed-related hospitalizations, orthopedic procedures, target joint incidence, and support physical activity without additional infusions or joint damage. RESULTS: Experts consistently recommended higher FLs for individuals with joint damage than for those without. Optimal average FLs ranged from 24% to 51%, exceeding traditionally recommended prophylactic trough levels (3-5%). Considerable uncertainty was noted around FLs for physical activity, reflecting the complexity of individualized care. CONCLUSIONS: Standard prophylaxis regimens may not provide sufficient protection for all patients, particularly those with joint damage. A personalized treatment approach, targeting higher FLs when necessary, may be critical for optimizing outcomes.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".