Reproducibility of automated and manual determination of the pulmonary artery to aorta ratio (PA:A) of ‘real-world’ CT scans
Bibliographic record
Abstract
The PA:A as a marker of acute exacerbations of COPD (AECOPD) was validated using research CT scans. Automation and validation using ‘real-world’ community-acquired CTs would facilitate uptake into clinical practice. We aimed to validate the reproducibility of the PA:A in a community practice-based study with raters (R) of varying levels of expertise and a semi-automated software (SlicerCIP, chestimagingplatform.org) against a thoracic radiologist (gold standard). Randomly selected CTs from a community practice database were anonymized. Two medical students (R1, R2) and a gynecologist (R3) performed between 4 and 3 consecutive randomized, blinded measures of the PA:A (trials). A general and a thoracic radiologist each performed 1 trial. R2 performed a Slicer-assisted trial. 33 CTs (18 contrast, 15 non-contrast) from 6 subjects (4 COPD, 1 asthma, 1 PH, 1M:5F, age 67 mean years(±16SD), PA:A ratio 0.86(±0.11)). Results are shown in Table1. <fig><object-id>erj;64/suppl_68/PA1657/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> The PA:A of ‘real-world’ CTs exhibit excellent intra- and inter-rater agreement and reproducibility between radiologists matching those of research CTs. With experience, clinicians obtain similar results. While Slicer-assisted performance is less than expert radiologist and trained clinician, it is better than untrained clinician. Whether the PA:A of ‘real-world’ CTs will be equally predictive for AECOPD as that of research CT scans remains to be determined.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".