Instruments for measuring the neuromuscular function domain of vitality capacity in older persons: an umbrella review
Bibliographic record
Abstract
PURPOSE: Recently, handgrip, knee extensor and respiratory muscle strength were proposed as candidate biomarkers to assess the neuromuscular function of vitality capacity in older persons. This umbrella review aims to provide an overview of the available instruments and their measurement properties to assess these biomarkers. METHODS: The databases PubMed, Web of Science and Embase were systematically screened for systematic reviews and meta-analyses reporting on handgrip, knee extensor or respiratory muscle strength assessments, resulting in 7,555 articles. The COSMIN checklist was used to appraise psychometric properties and the AMSTAR for assessing methodological quality. RESULTS: Twenty-seven systematic reviews were included in this study. Some of the identified reviews described the psychometric properties of the assessment tools. We found five assessment tools that can be used to measure neuromuscular function in the context of healthy ageing. Those are the handheld dynamometer for handgrip strength, the dynamometer for knee extensor strength and regarding respiratory muscle strength, the sniff nasal inspiratory pressure, maximal inspiratory pressure (MIP) and maximal expiratory pressure (MEP). CONCLUSION: The handheld dynamometer for hand grip strength, the dynamometer for knee extensor strength, sniff nasal inspiratory pressure, MIP and MEP were identified. Therefore, these assessments could be used to identify community-dwelling older adults at risk for a declined neuromuscular function in the context of vitality capacity.
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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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".