Efficacy of dual-layer spectral detector computed tomography for detecting early ischemic changes in patients with acute ischemic stroke: A pilot study
Bibliographic record
Abstract
Objectives: This study evaluated the efficacy of dual-layer spectral detector computed tomography (DLCT) for detecting early ischemic changes (EICs) in patients with acute ischemic stroke (AIS), focusing on electron density (ED) and effective atomic number (effective Z) imaging. Material and Methods: This retrospective study included 15 patients (mean age: 76.5 ± 9.8 years) with AIS who underwent non-contrast computed tomography (CT) with DLCT and magnetic resonance imaging (MRI) on the same day. Quantitative analysis was performed to compare conventional CT, ED, and effective Z values between the infarcted and contralateral brain regions. Qualitative assessment was conducted by two radiologists using the modified Alberta Stroke Program Early CT Score methodology. Receiver operating characteristic curve analysis was performed to evaluate diagnostic performance, and kappa statistics were used to assess interobserver agreement. Results: = 0.46) between the infarcted and contralateral regions. ED imaging demonstrated superior diagnostic accuracy (area under curve [AUC] = 0.90) compared with conventional 120-kVp CT (AUC = 0.85) and effective Z imaging (AUC = 0.62). Furthermore, interobserver agreement (kappa = 0.71) was better for ED imaging than for conventional 120-kVp CT (kappa = 0.65). Qualitative analysis revealed that ED images showed better agreement with MRI findings and higher interobserver consistency than conventional 120-kVp images. Conclusion: Compared with conventional CT, DLCT with ED imaging significantly enhanced detection of EICs in AIS.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".