Various regimens for prophylactic treatment of patients with haemophilia
Bibliographic record
Abstract
Haemophilia prophylaxis is superior to on-demand treatment to prevent joint damage. 'High-dose prophylaxis' as used in Sweden is more effective in preventing arthropathy than an 'intermediate-dose regimen' (the Netherlands) and the Canadian tailored primary prophylaxis. Prophylaxis may reduce the risk of developing inhibitors. There is no difference in inhibitor risk between plasma derived and recombinant factor VIII (rFVIII) products but the Rodin study showed increased risk with second-generation rFVIII products. MRI is a new and very sensitive tool to detect the symptoms of early arthropathy but some results (soft tissue changes in 'bleed-free joints') still need to be investigated. Ultrasound is a very helpful method to aid diagnosis especially during the acute phase of a bleed. The risk of infection with central venous access remains a matter of debate. A fully implanted central venous access device (CVAD) has a significant lower risk of infection compared to external CVADs. Patient's age under 6 yr and inhibitor presence are additional risk factors for infections. The role of arteriovenous fistulae needs to be investigated because significant complications have been reported. Disease-specific quality of life instruments are complementary to generic instruments evaluating QoL in patients with haemophilia and have become important health outcome measures.
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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 | 0.002 |
| 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) | 0.009 | 0.003 |
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".