Performing and interpreting individual pharmacokinetic profiles in patients with Hemophilia A or B: Rationale and general considerations
Bibliographic record
Abstract
OBJECTIVES: In a separate document, we have provided specific guidance on performing individual pharmacokinetic (PK) studies using limited samples in persons with hemophilia with the goal to optimize prophylaxis with clotting factor concentrates. This paper, intended for clinicians, aims to describe how to interpret and apply PK properties obtained in persons with hemophilia. METHODS: The members of the Working Party on population PK (PopPK) of the ISTH SSC Subcommittee on Factor VIII and IX and rare bleeding disorders, together with additional hemophilia and PK experts, completed a survey and ranking exercise whereby key areas of interest in the field were identified. The group had regular web conferences to refine the manuscript's scope and structure, taking into account comments from the external feedback to the earlier document. RESULTS: Many clinical decisions in hemophilia are based on some form of explicit or implicit PK assessment. Individual patient PK profiles can be analyzed through traditional or PopPK methods, with the latter providing the advantage of fewer samples needing to be collected on any prophylaxis regimen, and without the need the for a washout period. The most useful presentation of PK results for clinical decision making are a curve of the factor activity level over time, the time to achieve a certain activity level, or related parameters like half-life or exposure (AUC). Software platforms have been developed to deliver this information to clinicians at the point of care. Key characteristics of studies measuring average PK parameters were reviewed, outlining what makes a credible head-to-head comparison among different concentrates. Large data collections of PK and treatment outcomes currently ongoing will advance care in the future. CONCLUSIONS: Traditionally used to compare different concentrates, PK can support tailoring of hemophilia treatment by individual profiling, which is greatly simplified by adopting a PopPK/Bayesian method and limited sampling protocol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".