The relationship between antithrombin administration and inflammation during veno-venous ECMO
Bibliographic record
Abstract
Veno-venous Extracorporeal Membrane Oxygenation (ECMO) is used in the most severe cases of respiratory failure and further exacerbates the patients' inflammatory status. Antithrombin is supplemented during ECMO for its anticoagulant effects, but it also deploys anti-inflammatory properties. In this pre-specified ancillary study of the GATRA trial [NCT03208270] we aimed to evaluate the relationship between antithrombin and inflammation during ECMO. Forty-six patients were included in the study, 23 were randomized to receive antithrombin to maintain a level of 80-120% (study group) and 23 were randomized not to be supplemented (control group). Anticoagulation was provided in both groups with heparin infusion. Six cytokines were measured at 5 timepoints from prior to ECMO start to 7 days after ECMO removal. Cytokines decreased during the study but overall were not very different in the two groups. Testing the interaction between the study group and timepoints suggests that the administration of antithrombin led to a more rapid decrease over time of IL-6, IL-1β, TNF-⍺ and Pro-ADM. Plasma levels of antithrombin (either endogenous or exogenous) were negatively associated with all cytokines. Inflammation decreases during ECMO but a causal effect of antithrombin administration on the reduction of inflammation (and its clinical relevance) must be confirmed by appropriately powered studies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".