The Role of Genetics and Environment in Lifting Force and Isometric Trunk Extensor Endurance
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Our understanding of what different back performance tests are measuring is limited. The purpose of this study was to investigate the relative contributions of genetics and unique and common environmental factors for 3 tests of back muscle performance in a classic twin analysis. SUBJECTS: The subjects were a population-based sample of 122 monozygotic and 131 dizygotic male twin pairs aged 35 to 69 years (mean=49.9, SD=7.7). METHODS: Variance component analysis was applied to estimate genetic and environmental influences on isokinetic and psychophysical lifting and isometric trunk extensor endurance test performance. The Cholesky decomposition genetic factor model was used to estimate genetic and environmental correlations of these variables. Path analysis was applied to study determinants of isokinetic and psychophysical lifting and isometric trunk extensor endurance test performance. RESULTS: Genetic effects accounted for 60%, 33%, and 5% of the total variance of isokinetic and psychophysical lifting forces and isometric trunk extensor endurance, respectively, and unique environmental factors accounted for 35%, 49%, and 61% of the variance. DISCUSSION AND CONCLUSION: Genetics had a dominant role in isokinetic lifting and unique environmental factors in isometric trunk extensor endurance. The relatively high role of genetics in lifting force suggests the potential to increase and sustain changes in back muscle force in the general population may be particularly challenging.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".