Therapeutic choices for patients with hemophilia and high‐titer inhibitors
Bibliographic record
Abstract
Effective treatment of bleeding episodes in hemophilia with high titer inhibitors (HTI) remains a challenge, despite the fact that the therapeutic armamentarium has expanded considerably over the past few years. Treatment safety has improved with the availability of porcine factor VIII (FVIII) and bypassing products such as recombinant factor VIIa (rFVIIa), and plasma-derived activated Prothrombin Complex Concentrates (aPCCs) that are virally inactivated. The major drawbacks of rFVIIa and aPCCs are their unpredictable hemostatic effect, lack of laboratory assays to monitor efficacy and dosing frequency, and the risk of thrombosis. The proceedings of a one-day workshop of physicians who specialized in treating patients with hemophilia held in Vienna on May 13, 2000 have been summarized. In making a decision regarding the choice of product, physicians often consider the type of bleeding episode (life or limb threatening), age of the patient, volume of the reconstituted product, previous exposure to plasma derived products, cost, efficacy, and safety. For plasma naïve patients, to achieve rapid hemostasis a majority of the panelists used porcine FVIII (for patients who lack porcine inhibitory antibodies) or rFVIIa. For patients previously treated with plasma derived factors, in addition to the above concentrates, aPCCs were recommended. Although no data exists regarding safety and efficacy, switching products was routinely practiced either because of availability or cost. Furthermore, the panelists were uncertain about the efficacy of bypassing agents in the prevention of joint disease in inhibitor patients. The workshop participants felt that future research offers the best solution to resolve some of the dilemmas faced by clinicians and may help individualise treatment in a hemophilia patient with a high titer inhibitor.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 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".