Optimising musculoskeletal care for patients with haemophilia
Bibliographic record
Abstract
Despite recent improvements in the quality of care and treatment outcomes for haemophilia, joint disease remains a major concern for patients with and without inhibitors. Most bleeding episodes occur in the musculoskeletal system, and recurrent bleeding may result in progressive joint damage, leading to haemophilic arthropathy. Consequently, early identification and management of musculoskeletal bleeding episodes are important to prevent crippling deformities and dysfunction. Management strategies should aim at optimising joint function by reducing the frequency of, and preventing, joint bleeds. Although prophylactic factor replacement is proven to be effective in reducing bleeding frequency into joints and preserving musculoskeletal function in patients without inhibitors, the role for prophylaxis (with bypassing agents) in patients with inhibitors remains unclear. The available bypassing agents, activated prothrombin complex concentrate and recombinant activated factor VII (rFVIIa), are currently the standard of care for acute bleeding episodes in patients with high-titre inhibitors. These agents also prevent bleeding during elective orthopaedic surgery (EOS) in this patient population. This review discusses published data and uses illustrative cases to describe effective strategies for assessing joint health and maintaining optimal musculoskeletal care, focusing on the use of rFVIIa for haemostatic control in haemarthroses and when EOS is required in patients with inhibitors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".