[Predictive equations and analysis of influence factors of lung ventilation based on a large occupational population in North China].
Bibliographic record
Abstract
OBJECTIVE: To establish suitable predictive equations of lung function for occupational population in North China. METHODS: A total of 5 002 on the job or retired healthy adults from five enterprises in North China with category of mild or moderate work intensity underwent spirometry using a Chest HI-198 spirometer and the procedures recommended by the American Thoracic Society, were a sample. RESULTS: The data of 3 913 subjects were used. A normal distribution of our data was shown using the normality test and distribution curve. Univariate analysis showed that both age and height were significantly correlated with FVC, FEV(1), FEV(1)/FVC (%) and MMF. Further multiple linear stepwise regression analysis indicated that the levels of FVC, FEV(1), FEV(1)/FVC (%) and MMF were highly influenced by age, height, and weight rather than chest circumference. Thus, only age, height, and weight were introduced into our regression equations. Data from the studied subjects and other source were utilised to examine the validity of the equations and a high accordance rate (> 90%) was obtained. No significant difference (P > 0.05) was found in the predictive values between the simplified equations and equations in which more variables were included. CONCLUSION: The studied predictive equations for male non-smokers, female non-smokers, and male smokers were established based on data from a large occupational population. These equations should be more applicable for evaluating lung ventilatory function of occupational populations in North China.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".