Recommendations for Reducing Heterogeneity in Handgrip Strength Protocols
Bibliographic record
Abstract
Handgrip dynamometers are widely used to measure handgrip strength (HGS). HGS is a safe and easy to obtain measure of strength capacity, and a reliable assessment of muscle function. Although HGS provides robust prognostic value and utility, several protocol variants exist for HGS in clinical settings and translational research. This lack of methodological consistency could threaten the precision of HGS measurements and limit comparisons between the growing number of studies measuring HGS. Providing awareness of the protocol variants for HGS and making suggestions to reduce the implications of these variants will help to improve methodological consistency. Moreover, leveraging recent advancements in HGS equipment may enable us to use more sophisticated HGS dynamometer technologies to better assess muscle function. This Special Article will 1) highlight differences in HGS protocols and instrumentation, 2) provide recommendations to better specify HGS procedures and equipment, and 3) present future research directions for studies that measure HGS. We also provided a minimum reporting criteria framework to help future research studies avoid underreporting of HGS procedures.
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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 | 0.470 | 0.775 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.019 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.018 | 0.010 |
| Research integrity | 0.021 | 0.021 |
| Insufficient payload (model declined to judge) | 0.033 | 0.016 |
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".