Motor unit number estimation and neuromuscular fidelity in 3 stages of sarcopenia
Bibliographic record
Abstract
INTRODUCTION: Loss of motor units (MUs) and alterations in MU properties are major factors in development of sarcopenia. The purpose of this study was to compare MU properties among 3 groups categorized as either pre-sarcopenic (n = 7), sarcopenic (n = 7), or severely sarcopenic (n = 5), all with similarly aged subjects (75-82 years). METHODS: Using decomposition-enhanced quantitative electromyography, MU number estimates and neuromuscular stability measures of near-fiber (NF) jitter and jiggle were derived in addition to contractile properties of ankle dorsiflexors. RESULTS: MU number estimates were similar across groups; however, maximal voluntary strength in the severe sarcopenia group was 27% and 37% less than the sarcopenic and pre-sarcopenic groups, respectively. Moreover, NF jiggle was 31% greater in the severe group compared with pre-sarcopenia, NF jitter was 43% greater in the severe group compared with the pre-sarcopenic group. CONCLUSION: Unlike MU number estimates, strength and MU stability differed across groups and related to degree of sarcopenia. Muscle Nerve 55: 676-684, 2017.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".