Evaluating quantitative approaches to dynamic susceptibility contrast MRI among carotid endarterectomy patients
Bibliographic record
Abstract
PURPOSE: To evaluate two dynamic susceptibility contrast (DSC) quantification methods in symptomatic carotid artery disease patients undergoing carotid endarterectomy (CEA) surgery by comparing methods directly and assessing the reliability of each method in the hemisphere contralateral to surgery. MATERIALS AND METHODS: Absolute cerebral blood flow (CBF) and volume (CBV) was calculated in putamen and sensorimotor gray matter of 17 patients using two methods: 1) The Bookend method that scales relative DSC images to CBV values calculated from the ratio of pre- and postcontrast T1-weighted images, and 2) the Tail-scaling method that uses the ratio of area under the tails of the venous and arterial concentration time-courses to scale the DSC images. RESULTS: There was a positive correlation between the methods with significant correlation post-CEA (P < 0.035). Intersession correlation was greater when using the Tail-scaling method contralateral to surgery (P < 0.004). CONCLUSION: We have demonstrated correlation between methods that is significant after surgery and have found that the Tail-scaling method produces better test-retest reliability than our implementation of the Bookend method. Results from this study suggest that DSC has the potential to measure hemodynamic changes after endarterectomy and future work is required to establish clinical value.
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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 | 0.015 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".