Infarct Evolution on MR-DWI After Thrombectomy in Acute Stroke Patients Randomized to Nerinetide or Placebo
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The neuroprotectant nerinetide has shown promise in reducing infarct volumes in primate models of ischemia reperfusion. We hypothesized that early secondary infarct growth after endovascular therapy (EVT) (1) may be a suitable surrogate biomarker for testing neuroprotective compounds, (2) is feasible to assess in the acute setting using sequential MRI, and (3) can be modified by treatment with nerinetide. METHODS: test and multiple linear regression. RESULTS: = 0.02) in the nerinetide group compared with placebo after adjusting for confounders. DISCUSSION: This study highlights the potential of using MR imaging as a biomarker to estimate the effect of a neuroprotective agent in acute stroke treatment. Patients with acute large vessel occlusive stroke exhibited appreciable early infarct growth both in the gray matter and the white matter after undergoing EVT. Acknowledging relatively small overall infarct volumes in this study, treatment with nerinetide was associated with slightly reduced percentage infarct growth in the white matter and basal ganglia compared with placebo in patients not receiving intravenous alteplase and had no effect on the total early secondary infarct growth. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov NCT02930018. CLASSIFICATION OF EVIDENCE: This study provides Class II evidence that for patients with acute large vessel ischemic stroke undergoing EVT, nerinetide did not significantly decrease early post-EVT infarct growth compared with placebo.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".