Regional leptomeningeal collateral score by computed tomographic angiography correlates with 3-month clinical outcome in acute ischemic stroke
Bibliographic record
Abstract
PURPOSE: The aim of the study is to assess the correlation between regional leptomeningeal collateral (rLMC) Scores calculated on computed tomography (CT) angiography following acute anterior circulation ischemic stroke, with 3-month clinical outcome measured as modified Rankin Scale (mRS) and Barthel Index (BI).MATERIALS AND METHODS: A total of thirty patients were studied as per the exclusion and inclusion criteria and after informed consent. Multi-phase CT angiography was carried out within 24 h of stroke onset, and collateral scoring was done using rLMC score along with Alberta stroke programme early CT (ASPECT) scoring. At 3 months, patients were followed up to evaluate the clinical outcome using mRS and BI. Statistical analysis was performed to find out the correlation between rLMC score, ASPECT score, and clinical outcome and for association with demographic parameters and stroke risk factors.RESULTS: A strong correlation was noted between ASPECT and rLMC scores (P < 0.001) and between rLMC scores and clinical outcome at 3 months (mRS and BI). Correlation with mRS (P < 0.001) was nearly as strong as that of BI on follow-up (P < 0.001). The ASPECT score also was a predictor of clinical outcome and showed correlation with mRS (P < 0.001) and BI (P < 0.001). No significant association was found between various stroke risk factors and demographic parameters with rLMC scores. The rLMC scoring system showed substantial inter-rater reliability with Kappa = 0.7.CONCLUSIONS: rLMC score in CT angiography correlates with ASPECT Score and clinical outcome at 3 months. Hence, this scoring system can be used for collateral quantification as may be of use in predicting short-term clinical outcomes.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".