The haemophilia joint health score for the assessment of joint health in patients with haemophilia
Bibliographic record
Abstract
INTRODUCTION: The haemophilia joint health score (HJHS) is a tool used to assess joint changes in patients with haemophilia. There is lack of consensus on the interpretation of HJHS scores and their clinical relevance. AIM: To evaluate available literature reporting HJHS changes over time and assess a possible cut-off value for clinically relevant outcomes and the ideal follow-up for a meaningful score change. METHODS: We conducted a literature search of studies published between 2011 and 2023 where the HJHS version 2.1 had been adopted to detect changes in joint health in patients with haemophilia. We focused on studies that assessed clinical relevance of HJHS changes, evaluated the use of cut-off values and reported a follow-up over time. RESULTS: Our search identified 213 publications of which 53 (25%) were deemed relevant for this review. Of these, 33 (62%) publications reported the total HJHS score and 20 (38%) reported a single joint HJHS score, while the way of reporting HJHS scores/change was highly variable. Ten publications (19%) assessed clinical relevance, but their methods of calculation differed (defining a cut-off score, measuring standardised response mean or minimal detectable change). The follow-up duration varied from 2 weeks to 8 years in these 10 studies. CONCLUSIONS: High variability in assessing HJHS change over time is the primary consequence of its low sensitivity, and the lack of consensus on interpretation and clinical relevance of the score. Therefore, more sensitive tools should be used alongside HJHS to better define the joint health status of patients with haemophilia.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".