Obesity in the global haemophilia population: prevalence, implications and expert opinions for weight management
Bibliographic record
Abstract
Overweight and obesity may carry a significant disease burden for patients with haemophilia (PWH), who experience reduced mobility due to joint inflammation, muscle dysfunction and haemophilic arthropathy. This review aimed to define the prevalence and clinical impact of overweight/obesity in the global population of PWH. A detailed literature search pertaining to overweight/obesity in haemophilia in the last 15 years (2003-2018) was conducted, followed by a meta-analysis of epidemiological data. The estimated pooled prevalence of overweight/obesity in European and North American PWH was 31%. Excess weight in PWH is associated with a decreased range in motion of joints, accelerated loss of joint mobility and increase in chronic pain. Additionally, the cumulative disease burden of obesity and haemophilia may impact the requirement for joint surgery, occurrence of perioperative complications and the prevalence of anxiety and depression that associates with chronic illness. Best practice guidelines for obesity prevention and weight management, based on multidisciplinary expert perspectives, are considered for adult and paediatric PWH. Recommendations in the haemophilia context emphasize the importance of patient education and tailoring engagement in physical activity to avoid the risk of traumatic bleeding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| 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) | 0.005 | 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".