Appraising the Validity of Tools to Measure Multijoint Leg Power: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To critically appraise the validity of tools used to measure maximum multijoint leg extension power in older individuals. DATA SOURCES: A systematic literature search was performed in 5 electronic databases: PUBMED, EMBASE, CINAHL, SPORTDISCUS, and PEDRO from inception and without limits on the year of publication. Secondary searches included hand searching of the reference lists. STUDY SELECTION: One author performed all the searches and identified relevant studies. A second author repeated the search to ensure that no articles were overlooked. Only studies that measured multijoint leg extension power were included. Those that used jump tests on force plates were excluded. Forty-five studies were identified that used 3 different tools. Three of these studies addressed the validity of the instruments and were included in the analyses performed by all the authors. Decisions made by consensus. DATA EXTRACTION: Critical analyses were based on the reference instrument used, reproducibility of methods, appropriateness of the statistical analysis, commercial availability of the tool, and potential conflicts of interests, including financial support. Decisions regarding the data analyses were made by consensus among all authors. DATA SYNTHESIS: We identified 3 tools all of which simulated recumbent bicycles. Two of the 3 identified tools are not commercially available. Each of the 3 included studies used correlational analysis to determine the validity of their tool, which does not describe the accuracy of the measured power in comparison to the reference standard. CONCLUSION: We were unable to identify a validated tool that measured maximum multijoint leg extension power that was appropriate for older individuals. Future research should address this important gap.
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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 | 0.122 | 0.449 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.016 | 0.011 |
| Bibliometrics | 0.030 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".