Correlation of Red Blood Cell Distribution Width with The Severity of Coronary Artery Disease
Bibliographic record
Abstract
Atherosclerosis causing occlusion of coronary vessels by building up of plaque leading to narrowing of vessels supplying heart and causing coronary artery disease, which is the leading and common cause of mortality around the globe. Objective: To find the correlation of Red Cell Distribution Width (RDW) and severity of Coronary Artery Disease (CAD) lesions Methods: A number of 280 patients in total, admitted to cardiology department who presented with chest pain and diagnosed as angina, positive treadmill test and who underwent angiography and were found to have CAD, were enrolled in the study over a period of one year. Modified Gensini score (MGS) is used for assessment of severity of CAD. Each patient was assessed in relation to severity of CAD using MGS scoring system. Results: Out of 280 patients, 218 were assigned to Group A and 70 were assigned to Group B. Mean age of presentation were (52.34± 13.90 vs 50.8± 11.63 years). Male subjects were predominant overall with ratio of 2.63:1. RDW was assessed and was seen to be significantly elevated in Group A in comparison to Group B (14.98± 1.59 vs 13.82 ± 0.91p= <0.0001). After assessing MGS in relation to severity of CAD, significant correlation was observed. RDW was found to be increasing as MGS score increased (14.46± 0.65 vs 14.98± 1.00 vs 15.02± 0.88, p= <0.0001) Conclusions: It was concluded that RDW is a parameter which is cost effective, very easy, readily and urgently available for the assessment and stratification of patients presenting with coronary artery 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".