Investigating interassay variability between direct oral anticoagulant calibrated anti–factor Xa assays: a substudy of the perioperative anticoagulation use for surgery evaluation (PAUSE) trial
Bibliographic record
Abstract
Background: Direct oral anticoagulant calibrated anti-factor Xa (FXa) assays can assess residual anticoagulant levels in patients requiring urgent procedures or surgery. However, previous studies have shown variability between anti-FXa levels determined by different instrument-reagent combinations. This may be related to use of lyophilized samples, direct oral anticoagulant-spiked plasma, or interlaboratory variation. Objectives: 1) Determine the interassay variability in anti-FXa levels using 3 common instrument-reagent combinations. 2) Determine if differences between these combinations are clinically relevant. Methods: Seventy apixaban and 59 rivaroxaban samples from participants in the Perioperative Anticoagulation Use for Surgery Evaluation trial were simultaneously tested using Biophen reagents on the BCS XP analyzer (Siemens), HemosIL reagents on the ACL TOP analyzer (Werfen), and Stago reagents on the STA CompactMAX analyzer (Diagnostica Stago). Interassay correlations were analyzed at the predetermined cutoff of 30 ng/mL and compared with median anti-FXa levels. Results: = 0.6531-0.9702). Anti-FXa levels were also significantly different between all instrument-reagent combinations in the < 30 ng/mL group. In the ≥ 30 ng/mL group, apixaban was significantly different in all combinations, while rivaroxaban only differed between Biophen/BCS XP and Stago/STA CompactMAX. 7.8% (10/129) of samples were discrepantly classified across the 30 ng/mL threshold. Conclusions: Anti-FXa levels determined by 3 common instrument-reagent combinations show moderate-to-very strong correlations with each other. Although there are statistically significant differences between median anti-FXa levels these differences are not clinically significant, and result in discrepant classification across the 30 ng/mL threshold in only 7.8% of samples.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".