Housing characteristics, home environmental factors, and pulmonary function deficit in Chinese children: Results from the Seven Northeast Cities (SNEC) Study
Bibliographic record
Abstract
To assess the effects of housing characteristics and home environmental factors on lung function of Chinese children, 6740 children (aged 6–16 years) were recruited from seven cities in Northeast China in 2012. Performance of lung function was determined by comparison of forced vital capacity (FVC), forced expiratory volume (FEV1), peak expiratory flow (PEF), and maximal mid-expiratory flow (MMEF). Multivariate regression models were used to evaluate the associations with lung function deficit. The results showed that housing conditions were associated with lung function deficit in children. The adjusted odds ratios were 0.47 (95% CI: 0.26–0.83) for FVC for “ping-fang” housing compared with “dan-yuan-lou-fang” housing and 2.90 (95% CI: 2.43–3.47) for FEV1 with home renovations completed within two years compared with counterparts. The linear regression models consistently showed a significant association of housing conditions and home environmental factors with lung function measurements across subjects. A residence taller than seven stories was negatively associated with FEV1 ( β = −55; 95% CI: −97 to −13). In conclusion, housing conditions and home environmental factors are particularly important to the development of lung function and respiratory health in children. These factors are concerning and action should be taken to improve them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".