A comparison of images generated from diffusion‐weighted and diffusion‐tensor imaging data in hyper‐acute stroke
Bibliographic record
Abstract
PURPOSE: To compare isotropic (combined diffusion-weighted image [CMB], apparent diffusion coefficient [ADC], TRACE, exponential ADC [eADC], and isotropically-weighted diffusion image [isoDWI]) and anisotropic (relative anisotropy [RA], fractional anisotropy [FA], and volume ratio [VR]) diffusion images collected with fast magnetic resonance (MR) diffusion-weighted (DWI) and diffusion-tensor (DTI) acquisition strategies (each less than one minute) in hyper-acute stroke. MATERIALS AND METHODS: Twenty-one patients suffering from ischemic stroke-imaged within six hours of symptom onset using both DWI and DTI-were analyzed. Regions of interest were placed in the ischemic lesion and in normal contralateral tissue and the percent difference in image intensity was calculated for all nine generated images. RESULTS: The average absolute percent changes for the isotropic strategies were all > 38%, with isoDWI found to have a difference of 50.7% +/- 7.9% (mean +/- standard error, P < 0.001). The ADC maps had the most significant difference (-42.4% +/- 2.0%, P < 0.001, coefficient of variation = 0.22). No anisotropic images had significant differences. CONCLUSION: Anisotropic maps do not consistently show changes in the first six hours of ischemic stroke; therefore, isotropic maps, such as those obtained using DWI, are more appropriate for detecting hyper-acute stroke. Anisotropic images, however, may be useful to differentiate hyper-acute stroke from acute and sub-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 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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".