Abstract TP141: The Modified Dwi-aspects As A New Method Closer To The True Infarct Volume In Acute Stroke
Bibliographic record
Abstract
Objective: The DWI-Alberta Stroke Program Early CT Score (DWI-ASPECTS) assessed using diffusion-weighted imaging can estimate the infarct volume in acute stroke. However, for 10 parts of DWI-ASPECTS, a small lesion as well as a large lesion are treated equally as a one score. Thus, if the lesions are small, conventional DWI-ASPECTS might overestimate the true infarct volume. We created the modified DWI-ASPECTS score as a new method closer to the true infarct volume, and studied which better conventional and modified DWI-ASPECTS to predict the true infarct core volume. Methods: Stroke patients treated with mechanical thrombectomy in our hospital from January 1, 2013 to December 31, 2019 were enrolled. The modified DWI-ASPECTS was defined to one point if more than half of one-part area of M1-6 have high intensity, and no point if less than half of one-part area of M1-6 have high intensity. The ischemic core volume was measured using 3-dimen-sional Slicer 4.10.2, an open source software platform. Results: 297 cases were enrolled (age; 75 [67-82], 195 [65%] men and initial NIHSS score 15 [9-21]). Ischemic core volume was 11 [3-37] ml. Score of conventional and modified DWI-ASPECTS were 8 [7-9] and 7 [5-9], respectively. The correlation coefficient (r) between ischemic core volume and score of conventional and modified DWI-ASPECTS were 0.773, and 0.832, respectively. Therefore, modified DWI-ASPECTS was superior to conventional DWI-ASPECTS to predict the infarct core volume. Conclusions: The modified DWI-ASPECTS should be more effective to predict the true infarct core volume compared to conventional DWI-ASPECTS in 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".