Health‐related quality‐of‐life treatments for severe haemophilia: utility measurements using the Standard Gamble technique
Bibliographic record
Abstract
Prophylaxis for haemophilia improves outcomes, but at a substantial cost. Cost-utility analysis balances improvements seen in health-related quality of life (HRQoL) against costs, with the purpose of aiding healthcare decision-making. This analysis uses a measure of HRQoL known as utility. The objective of this study was to measure HRQoL (utility) values for states of health that result from on-demand therapy or prophylaxis. The HRQoL for different health states (including target joint bleeding, different intensities of prophylaxis, and indwelling intravenous catheters [ports]) was measured for healthy adults (n=30), parents of haemophilic children (n=30), and adults with haemophilia (n=28). Parents and patients rated health states similarly. Healthy adults gave the lowest ratings. The following rank, in order of HRQoL, was obtained: prophylaxis (low > medium > high) > on-demand therapy > prophylaxis with port> prophylaxis with infected port > on-demand therapy with development of a target joint. We conclude that: (1) haemophilia and its treatment reduce HRQoL; (2) prophylaxis is preferred to on-demand therapy; (3) intravenous ports substantially reduce HRQoL; (4) and an intravenous port to provide prophylaxis is preferable to on-demand therapy if a target joint develops.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".