Study of Octaplex dosing accuracy: An in vitro analysis
Bibliographic record
Abstract
Prothrombin complex concentrates (PCC) are recommended for urgent warfarin reversal. However, disagreement exists regarding the proper dosing strategy (i.e. fixed vs. weight-based). We measured the in vitro effect of PCC dosing on international normalised ratio (INR) and factor activity. Plasma from warfarin-anticoagulated patients with stable INRs was collected. PCC doses of 1,000, 2,000 and 3,000 IU were added to the samples, and INR and factor activity were analysed before and after PCC. Twenty-three of thirty subjects enrolled had complete data for analysis. INRs were below 1.5 in all samples post-1,000 IU, and decreased further with subsequent doses (p<0.001). Factors II, VII, and X increased with consecutive doses (p<0.01). Linear correlation was seen between INR and factors II, VII and X. Factor IX did not increase consistently nor show correlation with INR reversal. Weight-based dosing was then estimated; INRs were all <1.2 (0.9-1.2) and activity >0.50 IU for factors II, VII and X (0.96-1.52, 0.51-1.45 and 0.81-1.38, respectively). Factor IX did not uniformly correct above 0.50 IU (0.31-1.31). We confirm in vitro that 1,000 IU of Octaplex(®) is able to correct INR to <1.5 but factors were not uniformly >0.50 IU until 2,000 IU, and not >1.00 IU until 3,000 IU. This suggests that INR correction alone may not accurately reflect factor activity, and lends support for weight-based dosing.
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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 | 0.012 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".