Fluid-attenuated inversion recovery preparation: not an improvement over conventional diffusion-weighted imaging at 3T in acute ischemic stroke.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Change in signal intensity due to acute ischemic stroke can be detected on diffusion-weighted (DW) images soon after symptom onset. Fluid-attenuated inversion recovery (FLAIR) DW imaging suppresses signal intensity from water and has been suggested to be better than conventional DW imaging as a diagnostic imaging technique in acute stroke. We compared the signal intensity-to-noise ratio (SNR) and contrast-to-noise-ratio (CNR) between ischemic and normal tissues by using these two sequences. METHODS: Twenty stroke patients underwent imaging less than 6 hours after stroke onset by using both acquisition methods. The SNR of six regions of interest in normal brain and one region in ischemic brain were compared on both DW imaging and FLAIR DW imaging. We also compared CNR in normal and ischemic tissues. The calculated apparent diffusion coefficient (ADC) maps from each acquisition technique were similarly assessed. RESULTS: The SNR was significantly lower for FLAIR DW imaging than for DW imaging (P < .05). The CNR between normal and ischemic tissue was also lower on FLAIR DW imaging (P < .05). SNR and CNR of the ADC maps were significantly different (P < .05) for all tissues except the putamen and white matter (for SNR and CNR) and globus pallidus (for CNR only). CONCLUSION: Ischemic tissue on FLAIR DW imaging was significantly less conspicuous than on DW imaging and potentially limits the clinical utility of this sequence.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".