Study of Octaplex Dosing Accuracy: An In Vitro Analysis
Bibliographic record
Abstract
Abstract Abstract 4335 Background: Supratherapeutic INRs are common with warfarin therapy, and increase serious bleeding risk. Octaplex is a prothrombin complex concentrates (PCC) that is recommended for urgent warfarin reversal. However, disagreement exists regarding proper dosing strategies (fixed versus weight-based). Objectives: We sought to measure the in vitro effect of Octaplex on INR and factor levels, and to characterize this relationship. Methods: Plasma samples from eligible patients on warfarin with stable INRs for ≥4 weeks were collected. Plasma volumes were calculated to approximate 1000, 2000 and 3000IU doses of Octaplex and these were added to the samples. INR and factor levels were measured pre- and post-Octaplex. Results: Twenty-three of the 30 subjects enrolled had complete data for analysis. INRs corrected <1.5 in all samples post-1000IU, and decreased further with subsequent doses (p<0.001). Similar changes occurred in factors II, VII, and X (p<0.01). Linear correlations were seen between INR and factors II, VII and X. Factor IX did not increase incrementally or show a correlation with INRs. Weight-based dosing was then estimated. All INRs were <1.2 (0.9–1.2), and factor levels >0.50IU for II, VII and X (0.96–1.52, 0.51–1.45 and 0.81–1.38, respectively). Factor IX did not uniformly correct >0.50IU (0.31–1.31). Conclusion: We confirmed in vitro that 1000IU of Octaplex was able to correct INR to <1.5 but factor activity for II, VII and X was not uniformly >0.50IU until 2000IU, and >1.00IU not until 3000IU. Our study suggests that INR correction alone may not appropriately reflect sufficient factor activity, and lends support for weight-based PCC dosing. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".