A time trade-off study in the UK, Canada and the US to estimate utilities associated with the treatment of haemophilia
Bibliographic record
Abstract
INTRODUCTION: Haemophilia is a rare bleeding disorder caused by a deficient or absent clotting factor, leading to frequent bleeding. Multiple intravenous (IV) infusions have been the standard prophylactic treatment; however, newer treatment options involve less frequent subcutaneous (SC) injections. To inform future health economic evaluations, this study applied the time trade-off (TTO) method for estimation of utilities associated with haemophilia treatment for both people with the disease and potential caregivers. METHODS: Using the TTO method, utilities were estimated through two online surveys distributed in the UK, Canada and the US. In survey 1 (S1), adults from the general population aged 18 years and above evaluated health states as if they were living with haemophilia themselves and were receiving treatment for the condition. In survey 2 (S2), adults from the general population with a child under the age of 15 years evaluated health states as if they were treating their child for haemophilia. The surveys assessed the following treatment aspects: frequency of treatment, treatment device and injection site reactions. RESULTS: In total, 812, 739 and 703 respondents completed S1 and 712, 594 and 527 completed S2 in the UK, Canada and the US, respectively. In both surveys, the treatment device was associated with the largest impact on utilities for both people with haemophilia and caregivers. Monthly SC injections with a prefilled pen-device were associated with a significant utility gain compared with SC injections with a syringe and IV infusions. In S1, a lower treatment frequency was preferred in all three countries, while in S2, a lower treatment frequency was preferred only in the UK. Avoiding injection site reactions was associated with a significant utility gain in both surveys, but only in the UK and Canada. CONCLUSIONS: The study suggests that the administration of haemophilia treatment in particular has an impact on utilities for both people and caregivers living with the disease. Thus, less complex and time-consuming treatment devices are expected to improve health-related quality of life. This can be further modified additively by less frequent administration. These results can inform future health economic analyses of haemophilia and haemophilia treatment.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".