Diastolic Function Assessment of Left and Right Ventricles by <scp>MRI</scp> in Systemic Sclerosis Patients
Bibliographic record
Abstract
Background Heart involvement is frequent although often clinically silent in systemic sclerosis (SSc) patients. Early identification of cardiac involvement can be improved by noninvasive methods such as MRI, in addition to transthoracic echocardiography (TTE). Purpose To assess the ability of phase‐contrast (PC)‐MRI to detect subclinical left (LV) and right (RV) ventricular diastolic dysfunction in SSc patients. Study Type Prospective. Population Thirty‐five consecutive SSc patients (49 ± 14 years) and 35 sex‐ and age‐matched healthy controls (48.6 ± 13.5 years) who underwent TTE and MRI in the same week. Field Strength/Sequence 5 T/PC‐MRI using a breath‐hold velocity‐encoded gradient echo sequence. Assessment LV TTE (E/E′) and LV and RV PC‐MRI indices of diastolic function (LV early and late transmitral [EM, EfM, AM, AfM] and RV transtricuspid [ET, EfT, AT, AfT] peak filling flow velocities and flow rates, as well as LV [] and RV [] peak longitudinal myocardial velocities during diastole) were measured. Statistical Tests Two‐tailed t‐test, Wilcoxon test, or Fischer test for comparison of variables between SSc and healthy control groups; sensitivity, specificity, receiver‐operating‐characteristic (ROC) area under the curve (AUC) to assess discriminative ability of variables. A P‐value <0.05 was considered statistically significant. Results TTE LV E/E′ and MRI EM/ and ET/ were significantly higher in SSc patients than in controls (8.27 ± 1.25 vs. 6.70 ± 1.66; 9.43 ± 2.7 vs. 6.51 ± 1.50; 6.51 [4.70–10.40] vs. 4.13 [3.22–5.75], respectively) and separated SSc patients and healthy controls with good sensitivity (68%, 71%, and 80%), specificity (85%, 94%, and 62%), and AUC (0.787, 0.807, and 0.765). LV EfM was significantly higher in SSc patients than in controls (347.1 ± 113.7 vs. 284.7 ± 94.6) as RVAfT (277 [231–355] vs. 220 [154–253] mL/sec) with impaired relaxation pattern (EfT/AfT, 0.95 [0.87–1.21] vs. 1.12 [0.93–1.47]). Data Conclusion MRI was able to detect LV and RV diastolic dysfunction in SSc patients with good accuracy in the absence of LV systolic dysfunction at echocardiography. Use of MRI can allow to better assess the early impact of myocardial fibrosis related to SSc. Level of Evidence 1 Technical Efficacy Stage 2
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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 | 0.000 |
| 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".