Predictive Equations for Lung Function Based on a Large Occupational Population in North China
Bibliographic record
Abstract
OBJECTIVES: The currently used predictive equations of lung function in North China were derived from early study and have not been updated for nearly two decades. METHODS: Using American Thoracic Society (ATS) standards, sex-specific spirometric predictive equations for forced vital capacity (FVC), forced expiratory volume in one second (FEV(1)), ratio of FEV(1) to FVC (FEV(1)%) and forced expiratory flow at 25-75% of forced vital capacity (FEF(25-75%)) were derived from 2,897 asymptomatic, lifelong non-smokers (1,208 males, 1,689 females) from a large occupational population in North China. Stepwise multiple regressions were carried out to identify the best predictors of lung function parameters and predictive equations. Independent variables considered for inclusion in predictive equations including age, height, weight and chest circumference were examined. RESULTS: Age and height were found to be necessary variables for all lung function parameters. Weight was a significant variable in only half of our equations. Chest circumferences (expired or inspired) was excluded as they are not practical in use. Data from 255 apparently healthy non-smokers were used to validate the equations by comparing percentage predicted values and proportion of subjects with normal predicted values with those from the study group, and a high accordance was obtained. Other equations published and used in North China do not appear to offer advantages over these equations. CONCLUSIONS: These newly developed predictive equations should ideally be applied to calculate lung function for adult individuals and populations as reference values 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 | 0.003 | 0.005 |
| 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 | 0.001 |
| 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".