Evaluating international Haemophilia Joint Health Score (HJHS) results combined with expert opinion: Options for a shorter HJHS
Bibliographic record
Abstract
INTRODUCTION: The Hemophilia Joint Health Score (HJHS) was developed to detect early changes in joint health in children and adolescents with haemophilia. The HJHS is considered by some to be too time consuming for clinical use and this may limit broad adoption. AIM: This study was a first step to develop a shorter and/or more convenient version of the HJHS for the measurement of joint function in children and young adults with haemophilia, by combining real-life data and expert opinion. METHODS: A cross-sectional multicenter secondary analysis on pooled data of published studies using the HJHS (0-124, optimum score 0) in persons with haemophilia A/B aged 4-30 was performed. Least informative items, scoring options and/or joints were identified. An expert group of 19 international multidisciplinary experts evaluated the results and voted on suggestions for adaptations in a structured meeting (consensus set at ≥ 80%). RESULTS: Original data on 499 persons with haemophilia from 7 studies were evaluated. Median age was 15.0 years [range 4.0-29.9], 83.2% had severe haemophilia and 61.5% received prophylaxis. Median (IQR) HJHS total was 6.0 (1.0-17.0). The items 'duration swelling' and 'crepitus' were identified as clinically less informative and appointed as candidates for reduction. CONCLUSION: , which had the same discriminative ability. Additional steps are needed to achieve a substantially shorter HJHS assessment.
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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 | 0.061 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".