Neutrophil to lymphocyte ratio predicts island sign in patients with intracranial hemorrhage
Bibliographic record
Abstract
Our previously studies indicated that inflammatory responses are involved in the hematoma expansion (HE) after intracranial hemorrhage (ICH) ictus. Here, we aim to evaluate the correlations among the ratio of neutrophil to lymphocyte ratio (NLR), HE, and island sign in patients with ICH.Patients with spontaneous ICH were retrospectively included. Clinical characteristics, imaging features, and laboratory parameters were obtained. Multivariable analysis was performed to evaluate the association of NLR with HE or island sign. Receiver-operator analysis was also used to estimate their predictive abilities for HE and its imaging features.A total of 279 patients were enrolled in present study, and 78 patients had early hematoma growth, while 43 of them exhibited island sign. Elevation of both leukocyte (odds ratio [OR] 1.136, 95% confidence interval [CI] 1.037-1.245, P < .01) and neutrophil absolute numbers (OR 1.169, 95% CI 1.065-1.284, P < .01), as well as reduction of lymphocyte counts (OR 0.052, 95% CI 0.016-0.167, P < .01) were strongly associated with the existence of island sign. Moreover, despite the predictive ability of NLR on the existence of island sign (OR 1.063, 95% CI 1.036-1.090, P < .01), it also showed the best predictive accuracy (sensitivity 76.74%, specificity 79.66%, positive predictive value 40.70%, negative predictive value 94.90%, area under the curve 0.817) by comparing with peripheral leukocyte counts.The NLR could be used as an independently marker for reflecting the island sign in patients with ICH. Our findings indicated that systemic inflammatory responses might be involved in the pathologic process of active bleeding in cerebral.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".