Imaging of the brain in acute ischaemic stroke: comparison of computed tomography and magnetic resonance diffusion-weighted imaging
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Controversy exists about the optimal imaging technique in acute stroke. It was hypothesised that CT is comparable with DWI, when both are read systematically using quantitative scoring. METHODS: Ischaemic stroke patients who had CT within six hours and DWI within seven hours of onset were included. Five readers used a quantitative scoring system (ASPECTS) to read the baseline (b) and follow up CT and DWI. Use of MRI in acute stroke was also assessed in patients treated with tissue plasminogen activator (tPA) by prospectively recording reasons for exclusion. Patients were followed clinically at three months. RESULTS: bDWI and bCT were available for 100 consecutive patients (admission median NIHSS = 9). The mean bDWI and bCT ASPECTS were positively related (p<0.001). The level of interrater agreement ranged from good to excellent across all modalities and time periods. Bland-Altman plots showed more variability between bCT and bDWI than at 24 hours. The difference between bCT and bDWI was < or =2 ASPECTS points. Of bCT scans with ASPECTS 8-10, 81% had DWI ASPECTS 8-10. Patients with bCT ASPECTS of 8-10 were 1.9 times more likely to have a favourable outcome at 90 days than those with a score of 0-7 (95% CI 1.1 to 3.1, p = 0.002). The relative likelihood of favourable outcome with a bDWI ASPECTS 8-10 was 1.4 (95% CI 1.0 to 1.9, p = 0.10). Of patients receiving tPA 45% had contraindications to urgent MRI. CONCLUSION: The differences between CT and DWI in visualising early infarction are small when using ASPECTS. CT is faster and more accessible than MRI, and therefore is the better neuroimaging modality for the treatment of acute stroke.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".