Dynamic changes in lung water density and volume following supine body positioning
Bibliographic record
Abstract
PURPOSE: Measure the changes in relative lung water density (rLWD), lung volume, and total lung water content as a function of time after supine body positioning. METHODS: An efficient ultrashort-TE pulse sequence with a yarnball k-space trajectory was used to measure water density-weighted lung images for 25 min following supine body positioning (free breathing, 74-s acquisitions, 3D images at functional residual capacity, 18 time points) in 9 healthy volunteers. Global and regional (10 chest-to-back positions) rLWD, lung volume, and total lung water volume were measured in all subjects at all time points. Volume changes were validated with a nitrogen washout study in 3 participants. RESULTS: Global rLWD increased significantly (p = 0.001) from 31.8 ± 5.5% to 34.8 ± 6.8%, while lung volumes decreased significantly (p < 0.001) from 2390 ± 620 mL to 2130 ± 630 mL over the same 25-min interval. Total lung water volume decreased slightly from 730 ± 125 mL to 706 ± 126 mL (p = 0.028). There was a significant chest-to-back gradient in rLWD (20.7 ± 4.6% to 39.9 ± 6.1%) at all time points with absolute increases of 1.8 ± 1.2% at the chest and 5.4 ± 1.9% at the back. Nitrogen washout studies yielded a similar reduction in lung volume (12.5 ± 0.9%) and time course following supine positioning. CONCLUSION: Lung volumes during tidal breathing decrease significantly over tens of minutes following supine body positioning, with corresponding increases in lung water density (9.2 ± 4.4% relative increase). The total volume of lung water is slightly reduced over this interval (3.3 ± 4.0% relative change). Evaluation of rLWD should take time after supine positioning, and more generally, all sources of lung volume changes should be taken into consideration to avoid significant bias.
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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 | 0.000 | 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) | 0.001 | 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".