The relation of fragmented QRS with tissue Doppler derived parameters in patients with b-thalassaemia major
Bibliographic record
Abstract
PURPOSE: The most important complication encountered in patients with b-thalassaemia major is degenerative fibrosis developing as a result of iron accumulation in myocardial tissue. Dysfunction pursues this accumulation. Recently, presence of fragmented QRS (fQRS) in ECG has been regarded as a predictor of myocardial fibrosis. We aimed in our study to investigate the frequency with which fQRS develops in patients with b-thalassaemia major and to disclose the correlation between fQRS frequency and Doppler-derived indices. METHODS: The patients with b-thalassaemia major (n=66; mean age: 23±6 years) and healthy controls (n=30; mean age: 23±4 years) were included. fQRS pattern was described as presence of RSR' manifested as existence of additional R wave and notching in either R or S waves in ECG recordings. 2D, M-mode, conventional Doppler, tissue Doppler echocardiography parameters were assessed. Mean serum ferritin levels over past 5 years were also calculated. RESULTS: When compared to those in control group, fQRS was more frequent in b-thalassaemia major group, indicating statistical significance (p = 0.001). While E/Em and ferritin level exhibited statistically significant increase in thalassaemia patients with fQRS (p < 0.05), the mean Em and Sm values were found to be significantly low (p < 0.05). CONCLUSIONS: fQRS was frequently observed in the patients with b-thalassaemia major, which was of statistical significance. Tissue Doppler-derived diastolic and systolic indices in thalassaemia cases with fQRS showed statistically significant impairment compared to those without fQRS. In conclusion, fQRS may represent a novel noninvasive marker for cardiac involvement in patients with b-thalassaemia major.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".