Impact of Adopting Population Pharmacokinetics for Tailoring Prophylaxis in Haemophilia A Patients: A Historically Controlled Observational Study
Bibliographic record
Abstract
BACKGROUND: Performing individual pharmacokinetics (PK) studies in clinical practice can be simplified by adopting population PK-based profiling on limited post-infusion samples. The objective of this study was to assess the impact of population PK in tailoring prophylaxis in patients with haemophilia A. PATIENTS AND METHODS: Individual weekly treatment plans were developed considering predicted plasma factor activity levels and patients' lifestyle. Patients were trained using a visual traffic-light scheme to help modulate their level of physical activity with respect to factor infusions timing. Annualized joint bleeding rate (ABJR), haemophilia-specific quality of life questionnaire for adults (Haemo-QoL-A) and factor utilization were measured for 12 months before and after tailoring, compared within patients and analysed separately for those previously on prophylaxis (P), situational prophylaxis (SP) or on-demand (OD). RESULTS: = 0.003). The Haemo-QoL-A total score improved for 58% of P, 50% of SP and 29% of OD patients. Factor utilization (IU/kg/patient/year) increased by 2,400 (121; 2,586) for P, 1,052 (308; 1,578) for SP and 2,086 (1,498; 2,576) for OD. One of 138 measurements demonstrated a factor activity level below the critical threshold of 0.03 IU/mL while the predicted level was above the threshold. CONCLUSION: Implementing tailored prophylaxis using a Bayesian forecasting approach in a routine clinical practice setting may improve haemophilia clinical outcomes.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".