Global Comparative Antithrombin Field Study: Impact of Laboratory Assay Variability on the Assessment of Antithrombin Activity Measurement at Fitusiran Clinical Decision‐Making Points
Bibliographic record
Abstract
INTRODUCTION: Fitusiran is a subcutaneous, investigational small interfering RNA therapeutic that lowers antithrombin (AT) to increase thrombin generation and rebalance haemostasis in people with haemophilia A or B with or without inhibitors. AIM: To evaluate and compare the performance of commercially available in vitro diagnostic (IVD) AT activity assays. METHODS: Field study sample kits with plasma AT activity levels (100, 36, 14 and 9 IU/dL or % of normal) were created and distributed to global haemostasis laboratories. Values were assigned based on Siemens INNOVANCE AT activity assay using BCS-XP analyser. Reliability (relative accuracy estimate), intra- and inter-laboratory variability of IVDs in measuring AT activity in plasma samples using various commercially available AT assays was assessed. RESULTS: At normal AT activity level (i.e., 100%), all AT assays reliably measured AT activity with acceptable recovery. Accurate results were observed for all samples across sites using Siemens INNOVANCE AT assay. Increased variability was observed for all other assays at low AT levels. Siemens Berichrom and Stago STA-Stachrom assays accurately measured 100% and 36% AT activity; however, lab-to-lab variability was observed for ≤15% AT activity (CV >20%). All laboratories for the Stago STA-Stachrom assay failed to measure 9% AT activity. The HemosIL assay significantly underestimated AT activity levels ≤36%. There were no reported values for the 14% and 9% AT samples. CONCLUSIONS: Siemens INNOVANCE AT assay can reliably measure AT activity at clinical decision points of 15-35% of normal and is most suitable for clinical management of patients taking fitusiran.
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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 | 0.047 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".