Assessment of Myocardial Performance Index and Aortic Elasticity in Patients With Beta-Thalassemia Major
Bibliographic record
Abstract
BACKGROUND: This study aimed to assess myocardial performance index (MPI) and arterial elasticity indices in asymptomatic patients with beta-thalassemia major without known heart disease and to determine relationship between these indices and parameters indicating iron load of body. METHODS: The study included 55 asymptomatic beta-thalassemia patients (median age: 20 years (10 - 48 years)) without known history of heart disease and 40 age- and sex-matched healthy controls. MPI and arterial elasticity indices were determined by using standard two-dimensional and Doppler echocardiography. Data were analyzed by SPSS for Windows version 20.0 (SPSS Inc., Chicago, IL, USA). RESULTS: Left ventricular mass index (83.917 (50.62 - 144) and 68.37 (41.9 - 113.3)) and MPI (0.464 (0.33 - 0.68) and 0.431 (0.31 - 0.51)) were significantly higher in patients with beta-thalassemia when compared to control group (P < 0.001 and P = 0.006). Aortic elasticity indices were significantly higher while aortic strain and distensibility values were significantly lower in patients with beta-thalassemia compared to controls (all P values < 0.001). Positive correlations were detected between aortic stiffness index and platelet (r = 0.235; P = 0.019) and ferritin values (r = 0.328; P = 0.008). Presence of thalassemia (β = -0.729; P = 0.041) and higher platelet value (β = 0.235; P = 0.019) were significant determinants for increased aortic stiffness in linear regression analysis. CONCLUSION: Arterial elasticity indices and MPI are impaired in patients with beta-thalassemia major and these parameters may be used to predict cardiovascular complications in asymptomatic patients with beta-thalassemia major.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".