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.
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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 | 0.001 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".