Pharmacokinetic‐tailored approach to hemophilia prophylaxis: Medical decision making and outcomes
Bibliographic record
Abstract
BACKGROUND: Clinical application of population pharmacokinetics (popPK) is of increasing interest to patients with hemophilia, providers, and payers. Routine use of popPK profiles in factor replacement prophylaxis decision making has the potential to maintain or improve efficacy and reduce product consumption. AIM: To investigate the feasibility of implementation and longitudinal assessment of pharmacokinetic (PK)-tailored prophylaxis in routine clinical practice for hemophilia A and to describe factors that influence decision making for prescribed hemophilia prophylaxis. METHODS: This longitudinal, multicenter, prospective feasibility study of children and adults with hemophilia A without inhibitors used the Web Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) to generate PK profiles. Assessments over 12 weeks captured data on prescribed prophylaxis, popPK tool use, provider decision making, and patient-reported outcomes. RESULTS: Eighteen participants aged 6 to 39 years enrolled; half used extended half-life concentrates. Patient interest in their PK centered on general curiosity followed by a desire for participation in physical activity and decrease in infusion frequency. Providers used the WAPPS clinical calculator feature to simulate prophylaxis regimens under different dose, infusion, and trough conditions. Most targeted troughs were 1 to 3 IU/dL. The feasibility assessment demonstrated challenges with patient recruitment; however, the majority of participants successfully completed study assessments meeting feasibility targets. CONCLUSION: A larger-scale study powered to evaluate the impact of PK-tailored prophylaxis on clinical and patient-reported outcomes is feasible with study design modifications to support increased recruitment rate. Shared decision making incorporating patient and provider goals is important and facilitated by regimen simulations with the clinical calculator.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".