Cardiothoracic Imaging for Outcome Prediction in Chronic Thromboembolic Pulmonary Hypertension after Pulmonary Endarterectomy or Balloon Pulmonary Angioplasty: A Scoping Review
Bibliographic record
Abstract
There has been a rapid expansion in centers performing balloon pulmonary angioplasty (BPA) and pulmonary thromboendarterectomy (PTE) for chronic thromboembolic pulmonary hypertension (CTEPH). The purpose of this scoping review was to identify cardiothoracic imaging predictors of outcomes and to identify gaps to address in future work. A scoping review was conducted using the framework outlined by Arksey and O'Malley and Levac et al. in MEDLINE and EMBASE. The study protocol was preregistered in OSF Registries and performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews (PRISMA-ScR) guidelines. There were 1117 identified studies, including 48 involving pulmonary thromboendarterectomy (n = 25) and balloon pulmonary angioplasty (n = 23). CT was the most common preoperative imaging modality used (n = 21) and CT level of disease was the most reported imaging predictor of outcomes for pulmonary thromboendarterectomy. Although must studies evaluated hemodynamic improvements, imaging was of additional use in predicting clinically significant procedural complications after balloon pulmonary angioplasty, as well as mortality and long-term outcome after pulmonary endarterectomy. Predictors reported in MRI and digital subtraction angiography were less commonly reported and warrant multicenter validation. Cardiothoracic imaging may predict clinically significant outcomes after balloon pulmonary angioplasty and pulmonary thromboendarterectomy. Radiologists involved in the assessment of CTEPH patients should be aware of key predictors and future investigations could focus on multicenter validation and new technologies.
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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 | 0.017 | 0.091 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".