How do Cormic Index profiles contribute to differences in spirometry values between White and First Nations Australian children?
Bibliographic record
Abstract
Abstract Background Spirometry values of First Nations Australian children are lower than White children. One explanation relates to differences in the sitting‐height/standing‐height ratio (Cormic Index), as this accounts for up to half the observed differences in spirometry values between White children and other ethnicities. We investigated whether the Cormic Index of First Nations children differs from White children and if this explains the lower spirometry values of First Nations children. Methods First Nations children (n = 619) aged 8–16 years were recruited from nine Queensland communities. Their spirometry and Cormic Index data were compared to that of White children (n = 907) aged 8–16 years from the NHANES III dataset. Results FEV1 and FVC of First Nations children was 8% lower for children aged 8–11.9 years and 9%–10% lower for children aged 12–16 years. The Cormic Index was statistically lower in the First Nations 8–11.9 years group (median = 0.515, interquartile range [IQR]: 0.506–0.525) compared with White children (0.519, IQR: 0.511–0.527), and this difference was greater in the 12–16 years group (0.505, IQR: 0.492–0.516; 0.520, IQR: 0.510–0.529). Adjusting for age, sex, and standing height, lower Cormic Index of First Nations children accounts for 14% (95% confidence interval [CI]: 7%–21%) of FEV1 and 15% (95% CI: 8%–21%) of FVC differences in the younger group, and 26% (95% CI: 16%–37%) of FEV1 and 31% (95% CI: 19%–42%) of FVC differences in the older group. Conclusion Ethnic differences in Cormic Index partly account for why healthy First Nations Australian children have lower spirometry values than White children. As childhood spirometry values impact adult health, other contributing factors require attention.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".