Inhibitor Risk Stratification and Individualized Treatment in Patients With Nonsevere Hemophilia A: A Single-Institution Practice Audit
Bibliographic record
Abstract
Inhibitor risk in nonsevere hemophilia A increases with cumulative factor VIII (FVIII) exposure days and high-risk mutations. A standardized approach to minimize inhibitor risk is warranted. Following establishment of a systematic approach to reduce inhibitor risk in nonsevere hemophilia, we evaluated the uptake of these strategies into clinical practice. All adult males with nonsevere hemophilia A followed by British Columbia Adult Hemophilia Program from 2004 to 2016 were included in this retrospective audit. Quality-of-care indicators on inhibitor prevention were examined. Of 108 patients, 18 patients had high-risk FVIII mutations for inhibitor development. Rates of FVIII genotyping and 1-deamino-8-d-arginine-vasopressin (DDAVP) testing in mild patients without contraindications were both over 90%, although DDAVP was used for surgical prophylaxis in only 70% of procedures. Inhibitor testing and clinic visits occurred at a median interval of 22 months. Over 80% of patients with high-risk mutations had documentation and education on their inhibitor risk. Our practice audit demonstrated a high level of recognition and patient education of individual inhibitor risk. Impact of our standardized approach on the incidence of inhibitor development is yet to be determined.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.000 |
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".