Comprehensive elements of a physiotherapy exercise programme in haemophilia – a global perspective
Bibliographic record
Abstract
SUMMARY: Exercise programmes for people with haemophilia are usually designed and implemented to help manage the recovery after a haemarthrosis or a muscle bleed, or as a tool to help prevent bleeding episodes from occurring. In this article, we have identified individual components of exercise that are often applied as separate entities, but may also need to be implemented in concert for optimized impact. Although it may be necessary on occasion to bias an exercise programme towards one component over the others, it is important to recognize that the various elements of exercise are not mutually exclusive. Decreased flexibility, strength and proprioception, will result in an impairment of balance and a loss of function. Programme design should whenever possible be guided by proven methodology in terms of how each component is incorporated, and more specifically how long to perform the exercise for and how many repetitions should be performed. We recognize, however, that this is not always possible and that there is significant value in drawing from the experience of clinicians with specialized training in the management of haemophilia. In this study, both perspectives are presented, providing reference-based reviews of the mechanics of the various elements of exercise as well as the expert opinions of the authors. Research that has been completed using patients with conditions other than haemophilia may or may not have a direct application with the bleeding disorders population, but the programme design based on principles of tissue healing in addition to disease specific knowledge should be encouraged.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".