Value of MRI T2 FLAIR Vascular Hyperintensities Combined with DWIASPECTS in Predicting the Prognosis of Acute Cerebral Infarction withEndovascular Treatment
Bibliographic record
Abstract
OBJECTIVE: To explore the MRI T2 fluid-attenuated inversion recovery (FLAIR) vascular hyperintensities (FVH) combined with diffusion-weighted imaging (DWI) Alberta Stroke Program Early CT Score (ASPECTS) in predicting the prognosis of acute cerebral infarction (ACI) with endovascular treatment. METHODS: The patients with ACI in the anterior circulation who underwent endovascular treatment from June 2016 to December 2020 were divided into a good prognosis group and a poor prognosis group according to the modified Rankin Scale (mRS) score at 90 days after the operation. The differences in general clinical baseline data, CT-ASPECTS, FVH, and DWI-ASPECTS between the two groups were analyzed. The receiver operating characteristic (ROC) curve was used to analyze the predictive power of prediction models on prognosis. RESULTS: The results of the Binomial Logistic regression equation showed initial National Institute of Health stroke scale (NIHSS), Mori grade, DWI-ASPECTS, and FVH were independent risk factors for prognosis. The predictive power of the FVH + DWI-ASPECTS prediction model was highest, and the predictive power of DWI-ASPECTS was higher than that of CT-ASPECTS. CONCLUSION: DWI-ASPECTS is better than CT-ASPECTS in predicting the prognosis of ACI with endovascular treatment, and the combined prediction model of FVH and DWI-ASPECTS has higher prediction performance, which can be used as a preoperative evaluation method to predict the effect of endovascular treatment for ACI.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".