Identification of carotid plaque microcalcification by Micropure imaging
Bibliographic record
Abstract
Objective To explore the relationship between carotid plaque microcalcifcation and ischemic stroke (including transient ischemic attack) and the value of carotid microcalcification in predicting ischemic stroke. Methods Twenty-six patients in accordance with atherothrombosis models classified by Korean modified TOAST classification were enrolled in this study from November 2016 to March 2017. The microcalcification of the bilateral carotid was detected by Micropure imaging and the severity of intracranial ischemic focal lesions was evaluated by Alberta stroke programme early CT scale (ASPECT). The relation of ASPECT scores with microcalcification of the bilateral carotid was analyzed, and the value of carotid microcalcification in predicting ischemic stroke was analyzed by receiver operating characteristic curve method. Results Microcalcifition was detected in 27 of the total 52 carotids (51.92%) in 19 patients, which localized in the fibrous cap in 23 carotids (85.19%) and the basilar part of the plaque in 4 carotids (14.81%). The microcalcification surrounded the macrocalcifiation in 14 carotids (51.85%). The ASPECT scores were 10.85±1.43 in the microcalcifition side, which were significantly higher than those in the side without microcalcifition (11.80±1.19, t=2.584, P=0.013). The area under the curve was 0.673, with sensitivity of 0.667 and specificity of 0.680. Conclusion Micropure imaging maybe a new approach to detect the carotid microcalcification, and plaques with microcalcifition may easily cause ipsilateral ischemic stroke. Key words: Carotid; Microcalcification; Ultrsound; Stroke; Micropure imaging
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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 | 0.000 | 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) | 0.000 | 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".