Non-Factor Therapy Reduces Burden of Care for Caregivers of Children with Severe Hemophilia a: A Longitudinal Cohort Study Using the Hemophilia Family Impact Tool (H-FIT v1.1)
Bibliographic record
Abstract
Background: A child's hemophilia diagnosis has an impact on the caregiver. Caregivers often need to administer routine prophylactic therapy to prevent bleeds. Historically, in patients with severe hemophilia A (PWH-A), this consisted of giving intravenous (IV) clotting factor concentrate (CFC) multiple times per week. With the advent of novel non-factor therapies (e.g., emicizumab), there is an option to deliver prophylaxis subcutaneously (SC) less frequently. The Hemophilia Family Impact Tool (H-FITv1.1) is a caregiver-reported questionnaire that measures the burden of caring for a child with hemophilia. This validated tool can be used to evaluate how the burden of care changes when switching from IV to SC therapy. Aim: To evaluate the impact of emicizumab, as compared to IV clotting factor, on caregiver burden of care for PWH-A. Methods: Caregivers of PWH-A (0-18 years) on routine prophylaxis completed the H-FIT at four time points: baseline (day of switch to emicizumab), and at 3, 6, and 12-months post-switch to emicizumab. Institutional research ethics board approval and informed consent from all participants were obtained. The H-FIT is scored from 0 to 100, with 100 representing the lowest family burden. Results: Caregivers (N=35) of PWH-A (Mage=12.43 years, SD=4.15, 100% male, 97% infusing prophylaxis >twice/week pre-switch) completed the H-FIT. There was a non-statistically significant trend of improved burden of care following a switch to emicizumab (t11=-.81, CI=-14.86-6.86). Age stratification revealed this trend was consistent in children over the age of seven; however, the trend of improvement was not seen in the <7 years of age cohort. Conclusion: There was a trend of improved burden of caring for PWH-A in caregivers following the child's switch from IV to SC therapy. This trend was not seen in the under 7 cohort, however sample size was limited, and further studies are needed to elucidate age-related differences in care.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".