In Vivo 3D High-Spatial-Resolution MR Imaging of Intraplaque Hemorrhage
Bibliographic record
Abstract
PURPOSE: To apply magnetic resonance (MR) imaging of intraplaque hemorrhage (IPH), as compared with histologic analysis as the reference standard, to detect T1 hyperintense intraplaque signal and to test the hypothesis that T1 hyperintense material represents blood products (methemoglobin). MATERIALS AND METHODS: Institutional review board approval and patient informed consent were obtained. Eleven patients undergoing carotid endarterectomy were examined with MR imaging of IPH, and MR images were assessed for T1 hyperintense intraplaque signal. A total of 160 images per patient were available for coregistration with corresponding histologic slices. Because of endarterectomy specimen size and degradation and processing artifacts, only 97 images were coregistered to corresponding histologic slices. A grid that consisted of 16 segments was overlaid on images for correlation of MR images and histologic slices. Only one of 16 segments was chosen randomly per slide and used in the analysis. Agreement between MR images and histologic slices was measured with the Cohen kappa statistic. RESULTS: Strong agreement was seen between MR images and histologic slices, with T1-weighted high signal intensity corresponding to hemorrhagic material (kappa = 0.7-0.8). There was a low 2% false-negative rate for the detection of hemorrhage on the basis of T1-weighted hyperintensity (two of 97 measured segments). The results of diagnostic tests for T1 hyperintense detection of hemorrhage were as follows: sensitivity of 100%, specificity of 80%, positive predictive value of 70%, and negative predictive value of 100% for reader 1 and sensitivity of 94%, specificity of 88%, positive predictive value of 78%, and negative predictive value of 97% for reader 2. CONCLUSION: With its high spatial resolution, MR imaging of IPH permits detection of plaque hemorrhage location, resulting in strong agreement between imaging and histologic findings.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".