Assessment of haemophilic joints in a low‐resourced area using clinical tools: The effect of different types of prophylaxis
Bibliographic record
Abstract
INTRODUCTION: Haemophilic arthropathy (HA) is the most frequent complication in people with haemophilia (PWH). MRI is the gold standard to assess HA, however, there are limitations to its use in low-resourced areas. AIM: Primary; to compare clinical-functional, laboratory, and ultrasonographic joint scores with MRI scores to determine a reasonable alternative to MRI. Secondary; to identify the effect of various replacement therapies on the degree of joint involvement. MATERIAL AND METHOD: Fifty PWH with at least one affected joint, with or without inhibitors, and receiving either on-demand treatment or secondary prophylaxis, were included. All participants had a joint assessment by clinical HJHS 2.1, functional FISH, HEAD-US, and MRI DENVER scores. Also, serum COMP level was assessed by ELISA for the PWH and 50 healthy subjects as control. RESULTS: The HJHS 2.1 scores had a significant positive correlation with HEAD-US and the MRI DENVER scores. The FISH score had a significant negative correlation with HJHS 2.1, HEAD-US, and MRI DENVER Scores. The serum COMP level was comparable between the PWH and the controls. The HEAD-US score had a significant positive correlation with the MRI score. All of the joints' scores for the PWH on Emicizumab prophylaxis showed significantly lower HJHS 2.1 and MRI DENVER scores but higher FISH score than the joint scores of the patients receiving other types of prophylaxis. CONCLUSION: The clinical-functional joints assessment scores (HJHS 2.1, FISH) were objective tools that correlated significantly with the HEAD-US and the Denver MRI scores. Emicizumab prophylaxis led to better joint status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".