Peripheral Immune Cell Counts and Advanced Imaging as Biomarkers of Stroke Outcome
Bibliographic record
Abstract
INTRODUCTION: Circulating neutrophil and lymphocyte levels may be modifiable outcome predictors of ischemic stroke. We sought to compare these immune cell parameters with advanced imaging assessment and the 90-day clinical outcome. METHODS: We used a retrospectively collected cohort of consecutive ischemic stroke patients presenting within 4.5 h of symptom onset who had acute CT perfusion and routine blood collection before treatment with intravenous thrombolysis and 24-hour MRI scanning at the John Hunter Hospital. Full blood counts were performed acutely at 24 h and 7 days. Patient outcomes were assed at 90 days after stroke with the modified Rankin Scale (mRS). RESULTS: Overall, 142 patients were assessed during the study period. Patients with a poor outcome (mRS 3-6) had increased neutrophils (44% increase, p = 0.016), decreased lymphocytes (7% decrease, p = 0.491) and an increased lymphocyte-to-neutrophil ratio (196% increase, p < 0.001). Patients with good outcomes (mRS 0-2) did not have significant changes in their full blood counts. There was no relationship between the neutrophil count at 24 h and penumbral volume (r2 = 0.217, p = 0.212), reperfusion (r2 = 0.111, p = 0.085), or core growth (r2 = 0.297, p = 0.107). A backward multivariate analysis containing the 24-hour core volume and 24-hour neutrophil count was strongly related to the 3-month outcome (r2 = 0.477, area under the curve = 0.902, p < 0.001). CONCLUSIONS: Peripheral neutrophils have potential as a biomarker of outcome when used in conjunction with advanced imaging. Peripherally measured neutrophil counts change significantly over time after stroke and may be potential targets for immunomodulatory therapy in patients with a severe stroke or a large infarct volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".