Evaluation of Algorithms for the Treatment of Problem Bleeding Episodes in Patients With Hemophilia Having Inhibitors
Bibliographic record
Abstract
The correlation between real-world clinical decisions and adherence to published treatment algorithms for problem bleeding episodes in patients with severe hemophilia and inhibitors and the resultant impact on clinical outcomes were assessed. Nine cases documenting treatment for problem bleeding episodes in patients with severe hemophilia and inhibitors were retrospectively reviewed. Adherence to treatment algorithms was rated on a scale of 1 to 5, 1 being no adherence and 5 being very high adherence. Adherence ratings >3 were assigned to 7 cases in which high adherence was associated with ≤4 days to achieve hemostatic control; hospitalization for ≤7 days was noted in 6 of these cases. In cases rated ≤3 (n = 2), time to hemostatic control ranged from 5 to 8 days and hospitalization duration ranged from 10 to 16 days. These findings suggest that adherence to treatment algorithms may be beneficial in treating problem bleeding events in patients with hemophilia and inhibitors.
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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 | 0.014 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".