Home management of haemophilia
Bibliographic record
Abstract
The demonstrated benefits of home care for haemophilia include improved quality of life, less pain and disability, fewer hospitalizations, and less time lost from work or school. Although reduced mortality has not been demonstrated, the substantial increase in longevity since the early 1980s correlates with the introduction of home treatment and prophylaxis programmes. These programmes must be designed and monitored by haemophilia treatment centres (HTC), which are staffed with professionals with broad and complementary expertise in the disease and its complications. In return, patients and their families must be willing to accept the reciprocal responsibilities that come from administering blood products or their recombinant equivalents at home. Patients with inhibitors to factors VIII or IX pose special challenges, but these complications do not obviate participation in home care programmes. Home care was an essential prerequisite to the introduction of effective prophylactic factor replacement therapy. Prophylaxis offers significant improvements in quality of life, but requires a substantial commitment. The use of implantable venous access devices can eliminate some of the difficulty and discomfort of peripheral venous access in small children, but brings additional risks. The future holds the promise of factor concentrates for home use that have longer half-lives, or can be administered by alternate routes. Knowledge of patient genotypes may allow treatments tailored to avoid complications such as inhibitor development. Gene therapy trials, which are currently ongoing, will ultimately lead to gene-based treatments as a complement to traditional protein-based therapy.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.004 | 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".