Flexibility measurement of the knee flexors, a comparison of three clinical tests and isokinetic dynamometry
Bibliographic record
Abstract
The high incidence of recurrent hamstring injuries in sport, especially in the recent Summer Olympics, calls into question the accuracy of current measures of injury rehabilitation. Strength and flexibility differences between healthy and previously hamstring-injured athletes have been reported in the literature, but many studies have found no significant differences between the two groups using similar testing methods. Studies of the passive properties of skeletal muscle have reported the resistance to passive knee extension using a Kin/Com isokinetic dynamometer. Comparison of this flexibility measurement technique to other, more common measures of flexibility is not well documented in the literature. Also, there is little information in the literature with respect to the passive properties of in vivo skeletal muscle with a previous strain injury. The purpose of this study was to compare the measurement of flexibility by a Sit and Reach Test, Active Knee Extension Test, and Passive Knee Extension Test with the resistance to stretch during passive extension of the knee, as measured by the Kin/Com Isokinetic Dynamometer. A sub-problem was to examine the differences in flexibility measurement scores between individuals with a previous hamstring injury and individuals with no history of hamstring pathology. (Abstract shortened by UMI.)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".