Lung function in older humans: the contribution of body composition, physical activity and smoking.
Bibliographic record
Abstract
An allometric model was used to determine the important factors related to the decline in forced expiratory volume (FEV1.0) across ages 55-86 years in independently living men and women. Measurements were available from a randomized sample of 181 men and 203 women residing in London, Ontario, Canada. The effects of height, age, sex, adiposity, fat free mass (FFM), grip strength and physical activity (PA) on FEV1.0 were assessed using an allometric model to test the hypothesis that sex differences in lung function would be due in part to sex-related differences in the aforementioned variables and would therefore be eliminated by our analysis. The following model was linearized and parameters were identified using standard multiple regression: FEV1.0 = height(beta1) x FFM(beta2) x grip strength(beta3) x PA(beta4) x exp(beta0 + beta5age + beta6sex + beta7smoking + beta8%body fat) x epsilon. Results indicate that the amount of FFM and heavy intensity physical activity participated in by the elderly may be more important in influencing forced expiratory function than previously recognized. In addition, results from this study have confirmed the importance of age and height in the prediction of FEV1.0 and demonstrated a negative effect of smoking on lung function. Individuals with a greater FFM and physical activity level tended to be associated with an above average lung function performance. The cross-sectional rate of decline in FEV1.0 determined from our model was approximately 12% per decade.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".