Carotid Atherosclerotic Calcification Does Not Result in High Signal Intensity in MR Imaging of Intraplaque Hemorrhage
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Calcium can potentially shorten T1, generating high signal intensity in GREs. Because IPH appears as high signal intensity in MRIPH and the surface effects of calcium can potentially shorten T1 of surrounding water protons, the purpose of this study was to evaluate whether the high signal intensity seen on MRIPH could be attributed solely to IPH and not calcification. MATERIALS AND METHODS: Eleven patients undergoing carotid endarterectomy were imaged by using MRIPH. Calcification was assessed by scanning respective endarterectomy specimens with a tabletop MicroCT. MRIPH/MicroCT correlation used an 8-segment template. Two readers evaluated images from both modalities. Agreement between MRIPH/MicroCT was measured by calculating Cohen κ. RESULTS: High signal intensity was seen in 58.8% and 68.9% (readers 1 and 2, respectively) of MRIPH segments, whereas calcification was seen in 44.7% and 32.1% (readers 1 and 2, respectively) of MicroCT segments. High signal intensity seen by MRIPH showed very good but inverse agreement to calcification (κ = -0.90; P < .0001, 95% CI, -0.93 to -0.86, reader 1; and κ = -0.74; P < .0001; 95% CI, -0.81 to -0.69, reader 2). Most interesting, high signal intensity demonstrated excellent agreement with lack of calcification on MicroCT (κ = 0.92; P < .0001; 95% CI, 0.89-0.94, reader 1; and κ = 0.97; P < .0001; 95% CI, 0.96-0.99, reader 2). In a very small number of segments, high signal intensity was seen in MRIPH, and calcification was seen on MicroCT; however, these represented a very small proportion of segments with high signal intensity (5.9% and 1.6%, readers 1 and 2, respectively). CONCLUSIONS: High signal intensity, therefore, reliably identified IPH, known to describe complicated plaque, rather than calcification, which is increasingly recognized as identifying more stable vascular disease.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".