Prediction of Lung Function Status Using Handgrip Strength and Anthropometry among the Healthy Malay Population in Malaysia
Bibliographic record
Abstract
Lung function depends primarily on the strength of the intercostal muscles and the diaphragm, which is indirectly related to handgrip strength (HGS). This study aims to determine the predictability of lung functions using HGS among healthy adults of Malay ethnicity in Malaysia. This study also aims to compare the equation using HGS with equations without HGS, such as the Global Lung Initiative (GLI). This study was carried out among adults between 35 to 70 years of age residing in urban and rural Malaysia. A series of standardized questionnaires were used to collect socio-demographic information. Lung functions were measured using a portable spirometer and HGS was measured using a Jamar dynamometer. The predictability of lung function indices (FEV1 and FVC) using HGS, age, and height was determined using multiple linear regression (MLR). Prediction of lung function indices was also generated using models without HGS for comparison with the equation that used HGS from this study. Pearson correlation analysis showed that both dominant (r = 0.49; p < 0.001) and non-dominant (r = 0.58; p < 0.001) HGS had a moderate significant correlation with lung function. In the MLR model, HGS was a significant (p < 0.001) predictor of lung function indices (FEV1 and FVC). The correlation of the predicted and measured lung indices using the equation generated in this study, which includes HGS, was higher compared with other lung function test equations that do not include HGS. The equations from MLR could be used to predict lung function indices among healthy Malay adults. The measurement of HGS may be used as a screening tool for lung function status when spirometry is unavailable.
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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) | 0.001 | 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".