Effect of Lung Inflation State on Ventilation Defect Percent Measured using Hyperpolarized 129Xe MRI>
Bibliographic record
Abstract
Background: Hyperpolarized 129Xe magnetic resonance imaging(HP-MRI) is a novel technique shown to be useful in evaluating obstructive lung diseases(OLD). HP-MRI requires inhalation of a non-radioactive gas for calculating ventilatory defect percentage(VDP). In healthy adults, VDP is inversely related to lung inflation state(LIS) at time of imaging but this has not been studied in children or participants with OLD. Aim: To investigate how LIS affects VDP in participants with airway obstruction(AO). Methods: HP-MRI was conducted in a 31-year-old with moderate obstruction (FEV1pred of 65%, FEV1/FVC of 66%). Imaging was performed on a 3T MR system (Siemens Healthcare, Erlangen, Germany) and HP-MRI scans using a flexible vest coil (Clinical MR Solutions, WI, U.S.A.). Images were acquired over a 10-16 second breath-hold at 4 inflation states: residual volume(RV)+1/6 of total lung capacity(TLC), RV+1L, functional residual capacity(FRC)+1/6 of TLC, FRC+1L. Results: Figure 1A shows representative HP-MRI images with ventilation defects. Figure 1B shows VDP is greatest at RV+1/6 of TLC and inversely related with lung volume. Conclusions: This is the first of an ongoing study to assess the effect of LIS on VDP in patients with AO. In this participant, the VDP is largest at low LIS (RV+1/6 of TLC) likely due to air trapping. This underlines the importance of LIS standardization in HP-MRI to ensure accuracy of VDP measurements.
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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 | 0.001 |
| 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) | 0.002 | 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".