Prediction for the maximum inspiratory pressure value from the thoracic expansion measurement in Indonesian healthy young adults
Bibliographic record
Abstract
Background The diaphragm is the primary muscle responsible for breathing. Weakness in the diaphragm will result in breathing difficulties. The micro-RPM (respiratory pressure meter) is a non-invasive testing device to measure respiratory muscle strength, which is not always feasible, while thoracic expansion measurements are easy to do. Aim This study constructs a prediction formula for a maximal inspiratory pressure (MIP) value from thoracic expansion measurements. Methods This study was quantitative with a cross-sectional design. Participants were healthy adults aged 20--40 years, with normal Mini-Mental State Examinations, body mass index, spirometry, and moderate activity levels. The tests performed were MIP and thoracic expansion measurements at three levels: axilla (L1), the fourth intercostal space (L2), and at processus xiphoideus (L3). The data were analyzed using an unpaired t-test and multivariate. Results The mean MIP for males (81.51 ± 13.90 cmH~2~O) was significantly greater than females (63.17 ± 15.89 cmH~2~O) (P = 0.0001). These findings were not different with the Chinese, Indian, Mangalorean, and Malaysian populations because they are all of Asian ethnicity. Thoracic expansion L2 (r = 0.463, P = 0.0001) and L3 (r = 0.502, P = 0.0001) were moderately correlated with MIP, whereas thoracic expansion L2, L3 combined with gender had a weak effect on MIP. The prediction formula was: MIP = 56.802 + 2.387 + L2 + 13.904 + Gender \* and MIP = 53.289+ 3.561 + L3 + 9.504 + Gender \*, \* 0 = female; 1 = male. Conclusions A prediction formula for MIP can be made using the thoracic expansion variable with gender as a determinant factor. A quick and easy measurement of thoracic expansion can be used as a mean of screening respiratory muscle strength in patient care.
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.
How this classification was reachedexpand
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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".