Importance of computed tomography in defining segmental disease in chronic thromboembolic pulmonary hypertension
Bibliographic record
Abstract
BACKGROUND: Radiological assessment of patients with chronic thromboembolic pulmonary hypertension (CTEPH) is critical to decide whether patients should be treated with pulmonary endarterectomy (PEA). Although computed tomography pulmonary angiography (CTPA) is increasingly used for decision making in CTEPH, the value of CTPA to predict surgical findings and outcome has never been explored. METHODS: We retrospectively reviewed 100 consecutive patients with high-quality CTPA undergoing PEA for CTEPH between May 2015 and December 2017. The most proximal level of disease in the pulmonary artery on CTPA was classified by two blinded radiologists as level 1 (main pulmonary artery), 2a (lobar pulmonary artery), 2b (origin of basal segmental pulmonary artery), 3 (segmental pulmonary artery) or 4 (predominantly subsegmental pulmonary artery). RESULTS: CTPA demonstrated level 1 in 20%, level 2a in 43%, level 2b in 11%, level 3 in 23% and level 4 in 3%. A majority of males presented with level 1 (55%) and level 2 (57%), and a majority of females (83%) with level 3 (p=0.01). Levels 3 and 4 were associated with longer duration of circulatory arrest (p=0.03) and higher frequency of Jamieson type III disease at surgery (p<0.0001). Requirement for targeted pulmonary hypertension therapy after PEA was 28% at 3 years in level 2b/3/4 compared with 6% in level 2a and 13% in level 1 (p=0.002). Level 2b/3/4 was an independent predictor for targeted pulmonary hypertension therapy after PEA (hazard ratio 4.23, 95% CI 1.24-14.39; p=0.02). CONCLUSIONS: High-quality CTPA provides accurate evaluation of CTEPH patients. The level of disease on CTPA can help guide peri-operative planning and post-operative monitoring.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".