Clinical application of Web Accessible Population Pharmacokinetic Service—Hemophilia (WAPPS‐Hemo): Patterns of blood sampling and patient characteristics among clinician users
Bibliographic record
Abstract
BACKGROUND: Use of population pharmacokinetics (PopPK) to facilitate PK-informed prophylaxis in clinical practice has gained momentum among haemophilia providers due to the accessibility of tools such as the Web Accessible Population Pharmacokinetic Service-Hemophilia (WAPPS-Hemo) and availability of extended half-life (EHL) factor concentrates. It is unknown how clinicians implement PopPK. AIM: To investigate the evolution of PopPK use in clinical practice by comparing blood sampling strategies, patient features, and factor group between initial and recent periods of WAPPS-Hemo availability. METHODS: PK data for haemophilia A and haemophilia B patients from two time periods were extracted from the WAPPS-Hemo database: early availability (10/2015-09/2016) and recent use (10/2017-09/2018). We compared patient characteristics (age, body weight, haemophilia type), product type and dose, and blood sampling times between the time frames. RESULTS: Over 1900 eligible infusions were submitted to WAPPS-Hemo during the periods studied, with 85% representing FVIII concentrates. In the recent cohort, PK profiles were requested for younger patients (median age 18 vs 26 years), with increased proportional EHL FVIII use (29% vs 14% of infusions). High-use centres generally submitted fewer blood samples per infusion than non-high-use centres, although the number of samples collected by non-high-use centres decreased significantly over time. During both periods, blood sample timing was generally consistent with ISTH recommended windows. CONCLUSION: The use of WAPPS-Hemo by haemophilia providers grew by over threefold between the time periods investigated. While sampling times have included key time points proposed first by Björkman since early WAPPS-Hemo usage, a trend towards minimizing sampling was observed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".