Discordance Between Cross-Sectional and Longitudinal Studies for the Effect of Dust on COPD: Why?
Bibliographic record
Abstract
Regression analyses for the effect of an environmental agent on lung function often give discordant results when derived from cross-sectional compared with longitudinal studies. To evaluate why this occurs, a normal population was created by computer, and modeled to simulate functional change during life. Thus, factors known to influence lung function measurement (including those that may cause COPD) were manipulated experimentally so that their contributions to any discordance could be assessed. Regression analyses showed that significant discordance could be induced if the oldest birth cohort failed to reach the same maximal level of function as the youngest (a "cohort effect"). This distorted the cross-sectional (but not longitudinal) estimate for the dominating effect of age and additionally influenced cross-sectional estimates for the effects of partially collinear variables such as cumulative exposure to hazardous environmental dust. Discordance also occurred if regression coefficients became imprecise through random measurement/reporting error, between-subject variability, and differing susceptibility, but then the differences (sometimes marked) between cross-sectional and longitudinal estimates were not significant. We conclude that modeling a population with known characteristics can provide a useful means of demonstrating that cross-sectional versus longitudinal discordance may be fundamental and unavoidable (though explicable), or merely a consequence of imprecision.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".