Predictive value of CT pulmonary angiography to assess surgical accessibility for pulmonary endarterectomy in chronic thromboembolic pulmonary hypertension (CTEPH) patients
Bibliographic record
Abstract
Purpose: We aimed to assess the predictive value of a new radiological classification of the level of disease in CTEPH patients based on CT pulmonary angiography (CTPA) using surgical classification as standard of reference. Methods: 43 CTEPH patients (mean age 57 ± 16; 14 fem.) undergoing CTPA were retrospectively evaluated prior to surgery. The median time from CTPA to surgery was 77 days (range 1 to 248). 3 chest radiologists, blinded to surgical results, independently classified disease level based on the most prox. thrombus. Radiological and surgical classification was scored as follows: L1 (main pulmonary artery (PA)), L2a (lobar PA), L2b (lower lobe basal trunk), L3 (segmental PA), L4 (subsegmental PA). Fleiss kappa was used for interobserver variability. To assess the predictive value, “proximal disease” was defined as L1 and L2a, “distal disease” as L2b, L3, and L4. Results: 3 radiologists classified L1 in 35%, 28%, and 21%, L2a in 51%, 49%, and 61%, L2b in 7%, 14%, and 7%, and L3 in 7%, 9%, and 12%, respectively. None rated L4. Interobserver agreement was k=0.55. All radiological classifications were within 1 level of surgical classifications. Considering surgical classification as standard of reference, sensitivity, specificity, and accuracy of CTPA in identifying proximal disease in this CTEPH patient cohort is 89%, 70%, and 81%, respectively. Conclusion: CTPA is highly sensitive to predict the disease level in CTEPH patients with moderate interobserver agreement. This newly introduced imaging based classification may support surgeons’ decision-making and predict operative findings.
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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 | 0.007 |
| 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 | 0.001 |
| 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".