Yield of diffusion-weighted MRI for detection of potentially relevant findings in stroke patients
Bibliographic record
Abstract
OBJECTIVE: To determine whether diffusion-weighted imaging (DWI) could identify potentially clinically relevant findings in patients presenting more than 6 hours after stroke onset when compared with conventional MRI. METHODS: MRI with both conventional (T2 and proton density images) and echoplanar imaging (DWI and apparent diffusion coefficient maps) was performed 6 to 48 hours after symptom onset (mean, 27 hours) in 40 consecutive patients with acute stroke. All acute lesions were identified first on conventional images, then on DWI, by a neuroradiologist who was provided with the suspected lesion location, based on a neurologist's examination before imaging. Abnormalities were rated as potentially clinically relevant if they were detected only on DWI and 1) confirmed the acute symptomatic lesion to be in a different vascular territory than suspected clinically, 2) revealed multiple lesions in different vascular territories suggestive of a proximal source of embolism, or 3) clarified that a lesion, thought to be acute on conventional imaging, was not acute. RESULTS: The initial clinical impression of lesion localization was incorrect in 12 patients (30%). Clinically significant findings were detected by DWI alone in 19 patients (48%). DWI demonstrated the symptomatic lesion in a different vascular territory than suspected clinically or by conventional MRI in 7 patients (18%) and showed acute lesions in multiple vascular distributions in 5 patients (13%). In 8 patients (20%), DWI clarified that lesions thought to be acute on conventional MRI were actually old. CONCLUSION: In patients imaged 6 to 48 hours after stroke onset, DWI frequently provided potentially clinically relevant findings that were not apparent on conventional MRI.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".