Morphological Spectrum of Changes in Coronaries of Non-Cardiac Deaths: An Autopsy Study
Bibliographic record
Abstract
Introduction: Atherosclerosis is a major public health problem all over the world. Every year thousands of people die due to the causes related to cardiovascular system especially the coronary arteries. Atherosclerosis is characterised by fibro fatty changes in the intima of medium sized arteries. Aims & Objecctives: The aims and objectives of present study are to study the autopsy cases for atherosclerotic changes in coronaries of heart, its correlation with age and to study the morphology of such lesions. Materials & Methods: The study comprised of heart specimens from autopsy cases in which the cause of death was not related to cardiac cause. Gross and microscopic features were studied for any atherosclerotic changes. Data was compiled to look for prevalence of atherosclerosis in general population. American Heart Association classification was used to grade the lesions. Results: This study showed a significant number of cases with atherosclerotic changes varying from mild grade 1 lesion to severe complicated plaques. Age range was all the cases above 18 years of age. There was no limitation for upper age limit but in our study we got cases from 18 years to 65 years. Conclusion: Atherosclerotic changes can start at very early age. There is a need to modify lifestyle in order to avoid morbidity and mortality related to atherosclerosis.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".