The Use of Transthoracic Echocardiogram to Quantify Pulmonary Vascular Resistance in Patients with Systemic Sclerosis
Bibliographic record
Abstract
Objective. To explore the accuracy of tricuspid regurgitation velocity (TRV) to right ventricular outflow tract time-velocity integral (TVIRVOT) ratio by Doppler to determine pulmonary vascular resistance (PVR) in patients with systemic sclerosis (SSc). Methods. Thirty-five consecutive adult patients with SSc, fulfilling the 2013 European League Against Rheumatism/American College of Rheumatology classification criteria, with sinus rhythm referred for right heart catheterization (RHC), were retrospectively included. All patients underwent a transthoracic echocardiogram (TTE) performed within 24 h of RHC. Patients with SSc were recruited regardless of disease activity, cardiac symptoms, and treatment regimen. Doppler measurements were compared to RHC measurements. A linear regression equation was generated to predict PVR by echocardiogram based on the TRV/TVIRVOT ratio. The accuracy of Doppler measurements for predicting PVR > 3 Wood units was assessed by computing the areas under the receiver-operating characteristic curves. Results. There were 20 (57%) females in the study. The mean age was 65 ± 12 years. Mean and systolic pulmonary arterial pressures were 31 ± 8 and 53 ± 15 mmHg, respectively. There was a good correlation between TRV/TVIRVOT ratio assessed by Doppler and PVR measured by RHC (R = 0.743, p < 0.001). The equation generated by this analysis was the following: PVR by Doppler = 11.3 × (TRV/TVIRVOT) + 1.7. A cutoff value of 0.21 for TRV/TVIRVOT ratio provided the best sensitivity (86%) and specificity (86%) to determine PVR > 3 Wood units. Conclusion. Our study suggests that TTE using Doppler could be a useful and noninvasive tool for estimating PVR in patients with SSc.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".