Methodological challenges in studying the causal determinants of child growth
Bibliographic record
Abstract
Background: Previous studies of early life influences on later growth in childhood have varied in their analytical approaches, particularly with respect to 'adjustment' for differences in size at the beginning of the growth period examined. Methods: We compared three commonly used statistical models to assess the effect of maternal body mass index (BMI) on growth between 6.5 and 11.5 years in a large cohort of Belarusian children, as follows: (Model 1) analysis of the difference in anthropometric measurements between the two ages; (Model 2) analysis of the measurement at 11.5 years after adjustment for the same measurement at 6.5 years; and (Model 3) analysis of the difference in measurements after adjustment for the measurement at 6.5 years (mathematically identical to Model 2). Results: Among PROBIT children of obese mothers (BMI ≥ 30 kg/m 2 ) vs those of mothers with normal BMI (18.5 to < 25 kg/m 2 ), Model 1 yielded larger increases in most weight and adiposity outcomes than did Model 2. We show that these larger effects arise because Model 2 parameterizes the effect of maternal BMI twice in same model: once for its effect on size at 6.5 years, and a second time for its effect on growth over the 5-year period between 6.5 and 11.5 years. Similar results were obtained in analogous analyses from cohorts in Boston, MA, and Singapore. Conclusion: Analysing the effect of exposure on change in outcome between two ages (Model 1) is clearly preferable to 'adjustment' for the outcome at the earlier age whenever the exposure under study affects the outcome at the earlier age.
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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 | 0.289 | 0.522 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".