What is the Role for Population Pharmacokinetics in Hemophilia?
Bibliographic record
Abstract
Prevention of bleeding in hemophilia requires that plasma levels of the deficient factor exceed the desired minimum target level. Large interindividual variability suggests that knowledge of individual pharmacokinetic (PK) would help to achieve this goal, simultaneously minimizing infusion frequency and the amount of concentrate used. Population PK (PopPK) allows for the incorporation of determinants of interpatient variability and eliminates the need for extensive postinfusion plasma sampling. Barriers to implementation of PopPK are the need for concentrate specific models, Bayesian calculation power, specific expertise for validation and appraisal of forecasted estimates. The Web Accessible Population Pharmacokinetic Service – Hemophilia (www.wapps-hemo.org), developed by an international research network of hemophilia centers will test if PK-guided dose individualization can improve patient important outcomes in hemophilia.
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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 | 0.051 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".