Fitness, fatness, and estimated coronary heart disease risk: the HERITAGE Family Study
Bibliographic record
Abstract
KATZMARZYK, P. T., J. GAGNON, A. S. LEON, J. S. SKINNER, J. H. WILMORE, D. C. RAO, and C. BOUCHARD. Fitness, fatness, and estimated coronary heart disease risk: the HERITAGE Family Study. Med. Sci. Sports Exerc., Vol. 33, No. 4, 2001, pp. 585–590. Purpose: To determine the contributions of fatness and fitness to the estimated risk of future coronary heart disease (CHD). Methods: The sample consisted of 212 black and 411 white adult sedentary participants. Percent body fat (%BF) was measured using densitometry, whereas maximal oxygen uptake (V̇O2max) was measured on a cycle ergometer. Risk of future CHD was estimated using the revised Framingham Heart Study algorithm. Results: For fatness, the odds ratios for risk of future CHD were 1.83 and 1.70 for the moderate and high tertiles, respectively, compared with the low tertile. Similarly, the odds ratios for V̇O2max were 1.29 (NS) and 1.62, for the moderate and low tertiles, respectively. Removing V̇O2max from the full model had no effect; however, removing %BF resulted in a significantly weaker model (χ2 = 10.38, P < 0.01). Conclusion: Both fatness and fitness are important predictors of risk of future CHD, based on the Framingham index.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".