Developing a new scoring scheme for the Hemophilia Joint Health Score 2.1
Bibliographic record
Abstract
BACKGROUND: The Hemophilia Joint Health Score (HJHS) is a validated outcome tool developed for the assessment of joint health in people with hemophilia. The ordinal joint score assesses 9 items in 6 index joints. It is recognized as an optimal measurement of arthropathy in children and young adults. The aim of this study was to develop an updated scoring system for the HJHS that may overcome the limitations of its current ordinal scoring structure. METHODS: A survey was developed using 1000Minds decision-making software. Respondents were provided with discrete choice tasks of ranking alternatives to determine the preference weight, or relative importance, placed on different criteria for each HJHS item. The survey was distributed to an anonymous sample of health care professionals with extensive experience in the physical examination of joints in people with hemophilia. RESULTS: A total of 64 musculoskeletal health care professionals participated; with a 64% survey completion rate. The HJHS item weights provide a sum to 1.0; the highest-ranked item was extension loss (0.139) followed by swelling (0.121), whereas the lowest was duration of swelling (0.057) followed by muscle atrophy (0.08). Compared to the original, the relative efficiency of the new score was 5.4. CONCLUSIONS: Observed differences in preference weights for HJHS items highlight the potential under- or overestimation of true joint health using the current ordinal scoring system. An updated scoring system using weighted items may improve the precision of HJHS assessment, leading to improved clinical management of joint health, while providing a robust research tool.
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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 | 0.026 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".