Improved joint health in subjects with severe haemophilia A treated prophylactically with recombinant factor <scp>VIII</scp> Fc fusion protein
Bibliographic record
Abstract
INTRODUCTION: Joint arthropathy is the long-term consequence of joint bleeding in people with severe haemophilia. AIM: This study assessed change in joint health over time in subjects receiving recombinant factor VIII Fc fusion protein (rFVIIIFc) prophylaxis. METHODS: ALONG is the phase 3 pivotal study in which the benefit of rFVIIIFc as a prophylactic treatment for bleeding control was shown in previously treated severe haemophilia patients ≥12 years of age (arm 1: 25-65 IU/kg every 3-5 days, arm 2: 65 IU/kg weekly and arm 3: episodic). After completing ALONG, subjects had the option to enrol into the extension study (ASPIRE). This interim, post hoc analysis assessed changes in joint health over ~2.8 years in these patients. RESULTS: Forty-seven subjects had modified Haemophilia Joint Health Score (mHJHS) data at A-LONG baseline, ASPIRE baseline and ASPIRE Year 1 and Year 2. Compared with A-LONG baseline (23.4), mean improvement at ASPIRE Year 2 was -4.1 (95% confidence interval [CI], -6.5, -1.8; P = .001). Regardless of prestudy treatment regimen, subjects showed continuous improvement in mHJHS from A-LONG baseline through ASPIRE Year 2 (prestudy prophylaxis: -2.4, P = .09; prestudy episodic treatment: -7.2, P = .003). Benefits were seen in subjects with target joints (-5.6, P = .005) as well as those with severe arthropathy (-8.8, P = .02). The mHJHS components with the greatest improvement at ASPIRE Year 2 were swelling (-1.4, P = .008), range of motion (-1.1, P = .03) and strength (-0.8, P = .04). CONCLUSIONS: Prophylaxis with rFVIIIFc may improve joint health over time regardless of prestudy prophylaxis or episodic treatment regimens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".