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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".