Focusing in on use of pharmacokinetic profiles in routine hemophilia care
Bibliographic record
Abstract
BACKGROUND: Emergence of population pharmacokinetic models for prediction of individual pharmacokinetic (PK) profiles facilitates individualization of prescribed prophylactic therapy for patients with hemophilia A and B and may have a favorable impact on clinical outcomes and annual factor utilization. How providers approach the integration and application of these data into routine clinical practice is not clear. OBJECTIVE: To explore the potential application of and barriers to incorporating PK profiles into current hemophilia prophylaxis decision making. METHODS: A facilitated group discussion of hematologists practicing within the federally-supported United States Hemophilia Treatment Center Network was conducted. Separately, a group of parents of patients with severe hemophilia less than 18 years of age participated in a focus group on individualizing prophylactic factor regimens with the use of PK data. RESULTS: Physician participants constructed a conceptual model for factors that determined their selection of hemophilia prophylaxis. These factors clustered in five groupings. When charged with creating a prophylaxis regimen for a specific clinical case including PK data, eight of nine providers generated a unique regimen. Parent focus group supported PK data use as they preferred data driven treatment decisions. CONCLUSIONS: Clinician application of PK data for prophylaxis decision making is heterogeneous. Prospective evaluation of the use of PK-tailored prophylaxis in routine care and its impact on patient outcomes is needed. Parents perceived that, while obtaining blood draws could be challenging, images of factor activity decay informed their decisions about physical activity timing and provided an opportunity for partnership and shared decision making with their provider.
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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 | 0.044 | 0.119 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".