Magnetic Resonance Imaging Assists With Determining Etiology After Transient Ischemic Attack or Minor Stroke
Bibliographic record
Abstract
Background Magnetic resonance imaging infarct topography may assist with determining stroke etiology. The influence of diffusion‐weighted imaging (DWI)‐positive lesions on etiology determination in patients with transient ischemic attack or minor stroke is not well studied. Methods and Results We prospectively enrolled patients between 2010 and 2017 in 2 studies; participants with a final diagnosis of probable or definite transient ischemic attack or stroke were pooled for analysis. The primary outcome was the adjudicated ischemic etiology. We compared proportion of each etiology (cardioembolic, large‐vessel, small‐vessel disease, other) in patients who had DWI positivity compared with DWI negativity. We used logistic regression to determine the adjusted odds ratio (OR) for each etiology compared with undetermined by DWI positivity. The final analysis included 1498 patients: 832 (55.5%) were DWI‐positive. DWI‐positive patients were more likely to be diagnosed with small‐vessel disease (19.1% versus 5.3%) and less likely with undetermined etiology (36.9% versus 53.0%; P <0.001). After adjustment, the presence of any DWI lesion was associated with increased odds of assigning any etiology (OR, 1.8 [95% CI, 1.3–2.5]). A single DWI lesion was associated with increased odds of small‐vessel disease diagnosis (OR, 9.5 [95% CI, 6.4–14.0]), and multiple DWI lesions with reduced odds of small‐vessel disease (OR, 0.2 [95% CI, 0.1–0.4]) but increased odds of all other etiologies compared with undetermined etiology. Conclusions Any DWI‐positive lesion after suspected transient ischemic attack or minor stroke was associated with increased odds of assigning a etiology. Presence and topography of DWI lesions on magnetic resonance imaging may assist with etiology determination and may impact stroke prevention therapies.
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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".