Relationship of Alberta Stroke Program Early CT Score (ASPECTS) with the outcome of ischemic stroke and the neurocognitive stroke biomarkers
Bibliographic record
Abstract
Abstract Background Reliable and acceptable biomarkers are needed to anticipate the outcome and cognitive impairment following ischemic stroke. The goal of this research is to examine the association of ASPECTS with cognitive decline, biomarkers of stroke, and acute ischemic stroke outcomes. This study included 120 patients with ischemic stroke in the middle cerebral artery region. The initial NIHSS, non-contrast CT brain assessed by ASPECTS, and the biomarkers of cognitive decline such as ESR, CRP, S100B, MMP9, and glutamate were investigated. The Montreal Cognitive Assessment and modified Rankin scale (mRS) were evaluated after 3 months. Correlations between ASPECTS, MoCA, biomarkers of cognitive impairment, and mRS were done by Spearman correlation. Results The incidence of cognitive impairment in our patients was 25.8%. Stroke biomarkers (ESR, CRP, S100B, MMP9, and glutamate) were significantly increased in cognitively disabled individuals with significantly lower mean MoCA scores than in cognitively intact patients. There was a strong direct correlation linking the initial ASPECTS and total MoCA test score after 3 months follow-up. Cases with unfavorable outcomes were older, more incidence of hypertension, and had higher average initial NIHSS (P < 0.05). While the average ASPECTS scores for the favorable outcome group of patients were significantly higher and there was a significant negative correlation between the initial ASPECTS and modified Rankin Scale score. Conclusions ASPECTS is a reliable scale to identify the extent of acute ischemic injury and could participate in assessing the outcome. ASPECTS and particular neurocognitive stroke biomarkers will enable the early detection of post-stroke cognitive impairment. Trial registration Registration of Clinical Trial Research: ClinicalTrials.gov ID: NCT04235920
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".