Adapting Fitness Age Calculations to Suit a Modern North American Female Population, Regardless of Age, Race, or Ability Level
Bibliographic record
Abstract
Objectives: Fitness Age (FA) has been reported in Japan and South Korea as one way to assess an individual’s overall health. To date, this method has not been used in North America. The objective of the current study was to assess the applicability of existing Fitness Age calculations for North American women. Methods: Thirty-seven North American working women aged 18 to 67 years completed a fitness test battery, as described by previous studies. The fitness test results were used with published regression equations to estimate each woman’s FA which was subsequently adjusted to correct for biases in the data. Results: The data from the fitness tests were similar to those presented in the previously published literature with the exception of grip strength, trunk flexion, and body fat percentage which differed significantly compared to the populations assessed previously. These population differences appear to have impacted the applicability of the published methods for this population. Discussion: Because the FA estimates must be corrected according to a theoretical “ideal” to address population biases before they can be used, the authors propose using the deviation from this theoretical ideal rather than raw FA in future aging studies, as this is where the interesting inter-personal differences lay.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".