Preferences and Health-Related Quality-of-Life Related to Disease and Treatment Features for Patients with Hemophilia A in a Canadian General Population Sample
Bibliographic record
Abstract
BACKGROUND: Current treatments for hemophilia A in Canada include on-demand treatment as bleeds occur and regular intravenous prophylactic factor VIII (FVIII) infusions. The subcutaneous therapy emicizumab was recently approved for treatment of hemophilia A. The objective of this study was to estimate utility values associated with hemophilia A health and treatment states from a Canadian societal perspective, including preferences related to treatment efficacy and frequency and route of administration. METHODS: A vignette-based time trade-off (TTO) utilities elicitation was undertaken in Canadian adults to compare preferences for six hemophilia health states describing prophylactic and on-demand treatment, with varying bleed rates and frequency of treatment administration. A convenience sample was recruited via market research panels and snowball sampling. Health state/vignette definitions were informed by clinical experience, clinical trial results regarding bleed frequency, and validated by qualitative interviews of hemophilia patients and caregivers (n=10). Utilities were estimated via an online, trained interviewer-guided, vignette-based TTO exercise, where respondents valuated health states describing hemophilia patients (adults or children) receiving subcutaneous prophylaxis, intravenous prophylaxis, and on-demand treatments. Analyses included a descriptive analysis by health state; a mixed-effects analysis of utility values adjusted for subcutaneous vs infusion-based therapies and number of bleeds; and for prophylactic regimens, an analysis of utilities by frequency of infusions or injections. RESULTS: TTO interviews were conducted with 82 respondents. Mean utilities [95% CI] were highest for subcutaneous prophylaxis (0.90 [0.87-0.93]), followed by intravenous prophylaxis (0.81 [0.78-0.85]), and on-demand treatment (0.70 [0.65-0.76]). In regression analysis, subcutaneous treatment health states were associated with a utility increment of +0.1112. Additional bleeds and more frequent infusions were associated with lower utility values (-0.0027 per bleed and -0.0003 per infusion). CONCLUSION: Subcutaneous prophylaxis is associated with higher utility values compared to intravenous prophylactic and on-demand treatment, while increased bleeds and infusions are associated with reduced utility.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".