Systematic Review of Associations Between the Presence of Acute Ischemic Lesions on Diffusion-Weighted Imaging and Clinical Predictors of Early Stroke Risk After Transient Ischemic Attack
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Early risk of stroke after a transient ischemic attack can be reliably predicted with risk scores based on clinical features of the patient and of the ischemic event, but it is unclear how these features correlate with findings on brain imaging. METHODS: We performed a systematic review of the literature and identified all previous studies which reported patient characteristics and the nature of transient ischemic attack symptoms in relation to appearances on diffusion-weighted imaging (DWI). We then performed a meta-analysis of the associations between the components of the risk scores and positive DWI. Authors were contacted for additional unpublished data. RESULTS: Nineteen studies were identified by the systematic review, and additional unpublished data were obtained from 11 of these studies. On meta-analysis, several components of the risk scores were associated with positive DWI, including symptom duration > or =60 minutes (13 studies, odds ratio [OR], 1.50; 95% CI, 1.16 to 1.96; P=0.004), dysphasia (9 studies, OR, 2.25; 95% CI, 1.57 to 3.22; P<0.001), dysarthria (8 studies, OR, 1.73; 95% CI, 1.11 to 2.68; P=0.03) and motor weakness (9 studies, OR, 2.20; 95% CI, 1.56 to 3.10; P<0.001). However patient age, sex, hypertension and diabetes were not associated with the presence of DWI lesions. From an etiologic perspective, atrial fibrillation (9 studies, OR, 2.75; 95% CI, 1.78 to 4.25; P<0.001) and ipsilateral > or =50% carotid stenosis (10 studies, OR, 1.93; 95% CI, 1.34 to 2.76; P=0.001) were associated with positive DWI. CONCLUSIONS: Presence of acute ischemic lesions on DWI correlates with several clinical features known to predict stroke risk after transient ischemic attack. Large studies (sample size >1000) will therefore be required to determine the independent prognostic value of DWI and its interactions with these clinical characteristics.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".