Scintigraphic and MR perfusion imaging in preoperative evaluation for lung volume reduction surgery: pilot study results.
Bibliographic record
Abstract
PURPOSE: To compare MR perfusion imaging with perfusion scintigraphy in the evaluation of patients with pulmonary emphysema being considered for lung volume reduction surgery. PATIENTS AND METHODS: Six patients with pulmonary emphysema and two normal individuals were evaluated by MR perfusion imaging, perfusion scintigraphy, and selective bilateral pulmonary angiography. MR images were obtained with an enhanced fast gradient recalled echo with three-dimensional Fourier transformation technique (efgre 3D) (6.3/1.3; flip angle, 30 degrees; field of view, 45-48 cm; matrix, 256 x 160). The presence or absence of perfusion defects in each segment was evaluated by two independent observers. RESULTS: Using angiography as the gold standard, the sensitivity, specificity, and accuracy of MR perfusion imaging in detecting focal perfusion abnormalities were 90%, 87%, and 89%, respectively, while those of perfusion scintigraphy were 71%, 76%, and 71%, respectively. The diagnostic accuracy of MR perfusion imaging was significantly higher than that of scintigraphy (p<0.001, McNemar test). There was good agreement between two observers for MR perfusion imaging (kappa statistic, 0.66) and only moderate agreement for perfusion scintigraphy (kappa statistic, 0.51). CONCLUSION: MR perfusion imaging is superior to perfusion scintigraphy in the evaluation of pulmonary parenchymal perfusion in patients with pulmonary emphysema.
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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 | 0.004 |
| 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 | 0.001 | 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".