Effect of PCC on Thrombin Generation among Patients on Factor Xa Inhibitors with Major Bleeding or Needing Urgent Surgery (GAUGE): Design and Rationale
Bibliographic record
Abstract
Abstract Background Direct factor Xa inhibitors (FXaIs) account for most oral anticoagulant use and FXaI-associated bleeding events are common. Clinicians have variable national and regional access to specific FXaI reversal agents such as andexanet alfa. Many centers have adopted the use of prothrombin complex concentrates (PCCs) as hemostatic therapy for FXaI-associated major bleeding events. PCC does not impact circulating FXaI levels and its mechanism of action to achieve hemostasis in FXaI-associated bleeding is uncertain. While PCC increases quantitative thrombin generation assay (TGA) parameters, it does not correct FXaI-altered thrombin generation kinetics, nor does it normalize thrombin generation. Clinical data supporting the use of PCC are based on cohort studies reporting clinical hemostatic efficacy, which is difficult to measure. The benefits of PCC for FXaI-associated bleeding beyond supportive care are uncertain. Objective GAUGE is a prospective observational study designed to measure the effects of four-factor PCC administration (Octaplex) on TGA parameters among patients with FXaI-associated bleeding or needing urgent surgery. Methods Laboratory outcomes will include the mean paired change in TGA parameters from pre- to post-PCC administration and the proportion of participants whose post-PCC TGA values fall within a defined reference range. Clinical outcomes will include hemostatic efficacy, thromboembolic complications, and all-cause death at 30 days post-PCC. Conclusion Development of a viable and universally accessible FXaI bleed management strategy is crucial. GAUGE will provide in vivo data on the effects of PCC among patients with FXaI-associated bleeding.
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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 | 0.024 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".