Ethnically specific norms for ventilatory function
Bibliographic record
Abstract
Burney and Hooper1 recently explored the relationship between forced vital capacity (FVC) and subsequent mortality in Caucasians and African Americans aged 45–64 years from the Atherosclerosis Risk In Communities study. They found that compared with the Caucasians, the African Americans had lower age–height-adjusted FVC and higher age-adjusted mortality. Neither of these observations is novel (http://www.cdc.gov/nchs/data/databriefs/db64.pdf.)—indeed the first has led to the development of ethnic-specific spirometry norms such as the Global Lung Function Initiative equations.2 Burney and Hooper then predicted mortality in individuals, adjusting for FVC, ethnic group, age and other covariates. They developed three distinct models, using (i) raw FVC, (ii) percent predicted FVC (FVC%) based on NHANES reference equations for Caucasians, and (iii) FVC% based on NHANES ethnic-specific equations. The results for models (i) and (ii) showed no ethnic group differences in mortality, whereas model (iii) showed highly significantly greater mortality in the African Americans. These results led the authors to conclude that the use of ethnic-specific ventilatory function reference equations is inappropriate for assessing prognosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".