Quantification of the pulmonary vascular obstruction index on ventilation/perfusion lung scintigraphy: Comparison of a segmental visual scoring to the Meyer score
Bibliographic record
Abstract
Introduction: Quantifying the pulmonary vascular obstruction index (PVOI) is essential for the management of patients with pulmonary embolism or chronic thromboembolic pulmonary hypertension (CTEPH). The reference method for quantifying the PVOI with planar lung ventilation/perfusion (V/Q) scintigraphy is the Meyer score, which was validated using pulmonary angiography as a reference standard. However, it is complex to use in daily practice. In contrast, a rapid and fast quantification method consists in estimating the PVOI based on the number of segmental perfusion defects. However, the accuracy of this method has never been evaluated. In this study, we aimed to compare PVOI quantification on planar V/Q scintigraphy assessed by a segmental visual scoring (SVS) to the Meyer score. Materials and methods: The eligible study population consisted of consecutive patients who underwent planar V/Q scan for CTEPH screening. A central review was performed by three nuclear medicine physicians. PVOI was assessed by summing the number of segmental perfusion defects or equivalent (2 sub-segments = 1 segment = 5%) and by Meyer's method. The two interpretations were performed 6 months apart. A Spearman rank correlation coefficient was calculated to evaluate correlation between the two measurement methods. An intra-class correlation (ICC) was calculated to assess agreement. A Bland et Altman plot analysis was used to evaluate agreement between the two measurements. Results: A total of 226 V/Q scans were interpreted. Spearman rank correlation coefficient between SVS and Meyer was 0.963 (95%CI 0.952-0.971) for mismatched perfusion defects and 0.963 (95%CI 0.953-0.972) for perfusion defects regardless of ventilation. Intra-class correlation (ICC) for agreement was 0.978 (95%CI 0.972-0.983) for mismatched perfusion defects and 0.968 (95%CI 0.959-0.976) for perfusion defects regardless of ventilation. In Bland & Altmann analysis, the mean difference between the SVS method and the Meyer score was 0.42 and 0.61 for the mismatched or matched evaluation, respectively. Conclusion: Our study shows a high correlation, and low differences in PVOI quantification when using a segmental visual scoring (SVS) as compared to the Meyer score. The SVS has the great advantage to be easy and rapid to apply in daily practice.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".