Development of a single-session assessment protocol for isokinetic testing of the hamstrings/quadriceps strength ratio
Bibliographic record
Abstract
BACKGROUND: The ratio of hamstring to quadriceps strength (H/Q), from isokinetic testing, is often used to guide training or rehabilitation. OBJECTIVES: The primary objective was to develop an assessment protocol that produces reproducible H/Q data in a single testing session. METHODS: Twenty-four healthy subjects, 16 women and 8 men, were tested using a reliability study design (2 sessions, 5 repetitions). The main outcome measure was the dynamic control ratio at 60[Formula: see text]/s (DCR60). Testing was also done at 120[Formula: see text]/s, and concentrically at 180[Formula: see text]/s. Generalizability theory was used to determine the reliability ([Formula: see text]) coefficient and the minimal detectable change MDC, and to extrapolate the data to develop an optimal assessment protocol, ideally using data from only one session. RESULTS: Single-session DCR60 data were reproducible ([Formula: see text] coefficient [Formula: see text] 0.868, MDC95 [Formula: see text] 0.144) using 5 repetitions, excluding the first repetition from the average. Values were improved by averaging over multiple sessions and repetitions, although little was gained from more than 2 sessions or 5 repetitions. Comparable results were found for testing at 120[Formula: see text]/s. CONCLUSIONS: Reproducible DCR60 values may be derived from a single testing session of the hamstrings (in eccentric mode) and the quadriceps (in concentric mode). We recommend collecting five repetitions, and averaging the data for repetitions 2–5.
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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 | 0.031 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".