Relationship Between Diaphragm Thickness, Thickening Fraction, Dome Excursion, and Respiratory Pressures in Healthy Subjects: An Ultrasound Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Diaphragm ultrasonography is used to identify causes of diaphragm dysfunction. However, its correlation with pulmonary function tests, including maximal inspiratory (MIP) and expiratory pressures (MEP), remains unclear. This study investigated this relationship by measuring diaphragm thickness, thickening fraction (TF), and excursion (DE) using ultrasonography, and their relationship to MIP and MEP. It also examined the influence of age, sex, height, and BMI on these measures. METHODS: We recruited healthy Japanese volunteers and conducted pulmonary function tests and diaphragm ultrasonography in a seated position. Diaphragm ultrasonography was performed during quiet breathing (QB) and deep breathing (DB) to measure the diaphragm thickness, TF, and DE. A multivariate analysis was conducted, adjusting for age, sex, height, and BMI. RESULTS: Between March 2022 and January 2023, 109 individuals (56 males) were included from three facilities. The mean (standard deviation) MIP and MEP [cmH2O] were 72.2 (24.6) and 96.9 (35.8), respectively. Thickness [mm] at the end of expiration was 1.7 (0.4), TF [%] was 50.0 (25.9) during QB and 110.7 (44.3) during DB, and DE [cm] was 1.7 (0.6) during QB and 4.4 (1.4) during DB. Multivariate analysis revealed that only DE (DB) had a statistically significant relationship with MIP and MEP (p = 0.021, p = 0.008). Sex, age, and BMI had a statistically significant influence on relationships between DE (DB) and MIP (p = 0.008, 0.048, and < 0.001, respectively). CONCLUSION: In healthy adults, DE (DB) has a relationship with MIP and MEP. Sex, age, and BMI, but not height, are influencing factors on this relationship.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it