Bibliographic record
Abstract
Objective Beyond reducing inflammation and troponin T (TnT) release, the interleukin-6 receptor antagonist tocilizumab reduces neutrophil counts in patients with non-ST segment elevation myocardial infarction (NSTEMI). It is unclear if this is related to formation of neutrophil extracellular traps (NETs), carrying inflammatory and thrombotic properties. Methods In a placebo-controlled trial, 117 patients with NSTEMI were randomised to a single dose of tocilizumab (n=58) or placebo (n=59) before coronary angiography. The NETs related markers double-stranded DNA (dsDNA), myloperoxidase–DNA (MPO–DNA) and citrullinated histone 3 (H3Cit) were measured at five consecutive time points during hospitalisation (days 1–3). Results Our major findings were: (1) H3Cit levels were significantly higher in the tocilizumab compared with the placebo group at all time points (all p<0.05), and H3Cit area under the curve (AUC) was 2.3 fold higher in the tocilizumab compared with placebo group (p<0.0001). (2) MPO–DNA and dsDNA did not differ between the groups. (3) In both treatment arms, dsDNA AUC was associated with TnT AUC. (4) Neutrophil count AUC correlated inversely to H3Cit AUC (p=0.015) in the total population. Conclusions In patients with NSTEMI, treatment with tocilizumab is associated with increased circulating H3Cit levels, suggesting that tocilizumab enhances NETosis. Further studies should clarify whether NETosis is a relevant side effect of tocilizumab. Regardless of tocilizumab, dsDNA associated with TnT release, indicating a link between extracellular nuclear material and myocardial injury.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".