Impact of prophylaxis on health‐related quality of life of boys with hemophilia: An analysis of pooled data from 9 countries
Bibliographic record
Abstract
Background Prophylaxis reduces the frequency of bleeds in boys with severe hemophilia and is the standard care for their management in resource‐abundant countries. The effect of prophylaxis on Health‐Related Quality of Life (HRQoL) has not been established, because the sample sizes of most studies are too small to explore the relationship of multiple factors that influence HRQoL. Methods The aim of this study was to assess the impact of hemophilia severity and treatment regimen on HRQoL and to establish the minimum important difference (MID) using the international level of score distributions. HRQoL data were pooled from 7 studies across 9 countries. HRQoL was measured using the Canadian Hemophilia Outcomes–Kids' Life Assessment Tool (CHO‐KLAT). A mixed‐effect linear regression analysis was employed to assess the impact of prophylaxis on the CHO‐KLAT score. Results Data from 401 boys with hemophilia were analyzed (57.6% severe hemophilia and 57.6% receiving prophylaxis). The model revealed that receiving prophylaxis was significantly associated with higher HRQoL (regression coefficient 8.5, 95% confidence interval [CI] 3.9‐13.1). Boys with severe hemophilia had a significantly lower HRQoL as compared to boys with moderate and mild hemophilia whose CHO‐KLAT scores were 7.0 and 6.6 points higher, respectively. There was a significant interaction between treatment and disease severity ( P =0.023), indicating prophylaxis has the most significant impact in boys with severe hemophilia. Based on these pooled data, the MID of the CHO‐KLAT was established at 6.5. Conclusions This study confirms the positive effect of prophylaxis on HRQoL in boys with hemophilia in a real‐world setting and provides initial benchmarks for interpreting HRQoL scores based on use of the CHO‐KLAT instrument.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.023 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".