Perspective on strategies for matching across age and sex in physiology research: “recreationally active” is not good enough
Bibliographic record
Abstract
Cardiorespiratory fitness (CRF) and muscular fitness are powerful confounders in age and sex-related comparisons. This paper provides a perspective on the benefits and limitations of matching participants by physical activity behaviour, objectively measured fitness and normative fitness percentiles. Data presented herein are a subset of a larger study, and highlight that matching by physical activity, does not necessarily match on other metrics like physical fitness, especially when age-related comparisons are being made. Our data showed that young and older adults matched by physical activity behaviours showed the expected higher CRF and muscular fitness in male and younger participants, but older adults had higher CRF percentiles. This suggests that matching by physical activity behaviour may select older adults with relatively higher CRF. Researchers must choose their matching method carefully to ensure the appropriate aspects of fitness have been matched between groups. For clarity, they should also report when certain aspects of fitness have not been accounted for and give an explanation as to why.
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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 | 0.529 | 0.481 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.013 | 0.020 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".