Enhancing preoperative assessment in chronic thromboembolic pulmonary hypertension: A comprehensive analysis of interobserver agreement and proximity-based CT pulmonary angiography scoring
Bibliographic record
Abstract
Background: Surgical risk in chronic thromboembolic pulmonary hypertension (CTEPH) depends on the proximity of thromboembolism on CT pulmonary angiography (CTPA). We assessed interobserver agreement for the quantification of thromboembolic lesions in CTEPH using a novel CTPA scoring index. Methods: Forty CTEPH patients (mean age, 58 ± 16 years; 19 men) with preoperative CTPA who underwent pulmonary endarterectomy (PEA) (08/2020-09/2021) were retrospectively included. Three radiologists scored each CTPA for chronic thromboembolism (occlusions, eccentric thickening, webs) using a 32-vessel model of the pulmonary vasculature, with interobserver agreement evaluated using Fleiss' kappa. CT level of disease was determined by the most proximal chronic thromboembolism: level 1 (main pulmonary artery), 2 (lobar), 3 (segmental) and 4 (subsegmental), and compared to surgical level at PEA. Results: Interobserver agreement for CT level of disease was moderate overall (κ = 0.52). Agreement was substantial overall at the main/lobar level (κ, mean = 0.71) when excluding the left upper lobe (κ = 0.17). Though segmental and subsegmental agreement suffered (κ = 0.31), we found substantial agreement for occlusions (κ = 0.72) compared to eccentric thickening (κ = 0.45) and webs (κ = 0.14). Correlation between CT level and surgical level was strong overall (τb = 0.73) and in the right lung (τb = 0.68), but weak in the left lung (τb = 0.42) (p < 0.05). Radiologists often over- and underestimated the proximal extent of disease in right and left lung, respectively. Conclusions: CT level of disease demonstrated good agreement between radiologists and was highly predictive of the surgical level in CTEPH. Occlusions were the most reliable sign of chronic thromboembolism and are important in assessing the segmental vasculature.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".