Haemodynamics and serial risk assessment in systemic sclerosis associated pulmonary arterial hypertension
Bibliographic record
Abstract
The prognostic importance of follow-up haemodynamics and the validity of multidimensional risk assessment are not well established for systemic sclerosis (SSc)-associated pulmonary arterial hypertension (PAH). We assessed incident SSc-PAH patients to determine the association between clinical and haemodynamic variables at baseline and first follow-up right heart catheterisation (RHC) with transplant-free survival. RHC variables included cardiac index, stroke volume index (SVI), pulmonary arterial compliance and pulmonary vascular resistance. Risk assessment was performed according to the number of low-risk criteria: functional class I or II, 6-min walking distance (6MWD) >440 m, right atrial pressure <8 mmHg and cardiac index ≥2.5 L·min−1·m−2. Transplant-free survival from diagnosis (n=513) was 87%, 55% and 35% at 1, 3 and 5 years, respectively. At baseline, 6MWD was the only independent predictor. A follow-up RHC was available for 353 patients (median interval 4.6 months, interquartile range 3.9–6.4 months). The 6MWD, functional class, cardiac index, SVI, pulmonary arterial compliance and pulmonary vascular resistance were independently associated with transplant-free survival at follow-up, with SVI performing better than other haemodynamic variables. 1-year outcomes were better with increasing number of low-risk criteria at baseline (area under the curve (AUC) 0.63, 95% CI 0.56–0.69) and at first follow-up (AUC 0.71, 95% CI 0.64–0.78). Follow-up haemodynamics and multidimensional risk assessment had greater prognostic significance than at baseline in SSc-PAH.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".