Low‐dose factor VIII infusion in Chinese adult haemophilia A patients: pharmacokinetics evidence that daily infusion results in higher trough level than with every‐other‐day infusion with similar factor VIII consumption
Bibliographic record
Abstract
INTRODUCTION: Pharmacokinetics (PK) modelling suggests improvement of trough levels are achieved by using more frequent infusion strategy. However, no clinical study data exists to confirm or quantify improvement in trough level, particularly for low-dose prophylaxis in patients with haemophilia A. AIM: To provide evidence that low dose daily (ED) prophylaxis can increase trough levels without increasing FVIII consumption compared to every-other-day (EOD) infusion. METHODS: EOD infusions, each for 14 days was conducted at the PUMCH-HTC. On the ED schedule, trough (immediate prior to infusion), and peak FVIII:C levels (30 min after infusion) were measured on days 1-5; and trough levels alone on days 7, 9, 11 and 13. For the EOD schedule, troughs, peaks and 4-h postinfusion were measured on day 1; troughs and peaks on days 3, 5, and 7; troughs alone on days 9, 11 and 13 and 24-h postinfusion on days 2, 4 and 6. FVIII inhibitors were assessed on days 0 and 14 during both infusion schedules. RESULTS: Six patients were enrolled. PK evidence showed that daily prophylaxis achieved higher (~2 times) steady-state FVIII trough levels compared to EOD with the same total factor consumption. The daily prophylaxis had good acceptability among patients and reduced chronic pain in the joints in some patients. CONCLUSION: Our PK study shows low-dose factor VIII daily infusion results in higher trough level than with EOD infusion with similar factor VIII consumption in Chinese adult haemophilia A patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".