Quantification of Trapped Gas with CT and<sup>3</sup>He MR Imaging in a Porcine Model of Isolated Airway Obstruction
Bibliographic record
Abstract
PURPOSE: To quantify regional gas trapping in the lung by using computed tomographic (CT)-determined specific gas volume and hyperpolarized helium 3 ((3)He) magnetic resonance (MR) imaging in a porcine model of airway obstruction. MATERIALS AND METHODS: Four porcine lungs were removed after sacrifice for unrelated cardiac experiments, for which animal studies approval was obtained. Dynamic expiratory thin-section CT and (3)He MR imaging were performed during passive deflation from total lung capacity after obstructions were created with inverted one-way endobronchial exit valves in segmental or lobar bronchi to produce identifiable regions of trapped gas. Changes in specific gas volume were assessed from CT data for defined regions of interest within and outside of obstructed segments and for entire lobes. Helium 3 data were analyzed according to the corresponding regional signal reduction during expiration, compared with the total magnetic moment at each time point. RESULTS: In 4.5 seconds of free collapse, volume decreased by 6% +/- 2 (standard error) and 53% +/- 3, respectively, in trapped-gas lobes and in unobstructed regions (P < .0001). Specific gas volume changed by 6% +/- 2 in areas of trapped gas and decreased by 56% +/- 3 in unobstructed regions, from 3.4 mL/g +/- 0.2 to 1.5 mL/g +/- 0.1 (P < .0001). The (3)He signal intensity decreased by 25% +/- 6 and 71% +/- 3, respectively, in trapped-gas and normal regions (P = .0008). In unobstructed regions, the percentage decreases in specific gas volume and (3)He signal intensity were not statistically different from one another (P = .89). CONCLUSION: The results obtained from the model of gas trapping demonstrate that CT-determined specific gas volume and (3)He MR imaging can help identify and quantify the extent of regional trapped gas in explanted porcine lungs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".