Impact of different bilateral knee extension strengths on lower extremity performance
Bibliographic record
Abstract
ABSTRACT: Despite the impact of leg muscle strength on lower extremity motor performance-including walking and sit-to-stand transfer-it remains difficult to predict the relationship between bilateral leg muscle strength and lower extremity performance. Therefore, this study was designed to predict lower extremity function through the differential modeling of logarithmic and linear regression, based on knee extension strength.The study included 121 individuals living in the same community. The bilateral strengths of the knee extensors were measured using a handheld dynamometer, and the Timed Up & Go test (TUG) performance time and 5-m minimum walking times were assessed to predict lower extremity motor functions. Bilateral normalized knee extension muscle strengths and lower extremity motor function scores, including walking or TUG performance times, were assessed on the logarithmic and linear models. The Akaike information criterion (AIC) was used to evaluate the coefficient compatibility between the logarithmic regression model and the linear regression model.The AIC value for the linear model was lower than that for the logarithmic model regarding the walking time. For walking time estimation in the linear model, the coefficient value of knee extension strength was larger on the strong than on the weak side; however, the AIC value for the logarithmic model was lower than that for the linear model regarding TUG performance time. In the logarithmic model's TUG performance time estimation, the coefficient value of knee extension strength was larger on the weak than on the strong side.In conclusion, our study demonstrated different models reflecting the relationship between both legs' strengths and lower extremity performance, including the walking and TUG performance times.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".