Apples to apples? Discordant definitions still hinder evidence‐based treatments for sarcopenia
Bibliographic record
Abstract
Preservation of skeletal muscle strength, size and function is critical to healthy aging.The progressive loss of each with advancing age-sarcopenia-is deleterious on several levels.Exercise is the primary countermeasure for sarcopenia, but 'exercise' alone is a broad recommendation.In a recent publication in the Journal of Cachexia, Sarcopenia and Muscle, Shen and colleagues 1 aimed to compare the effectiveness of different exercise interventions for improving relevant patient outcomes (e.g., all-cause mortality, quality of life and falls) in older adults with sarcopenia.The authors summarized the effect of 12 interventions from 42 randomized controlled trials using a network meta-analysis (NMA).Resistance training (strength training) was the most effective exercise for improving quality of life, strength and physical performance, with or without additional exercise/nutritional interventions.The authors 1 deserve substantial recognition for their comprehensive work.In our view, their results support and reinforce resistance training as the primary countermeasure to sarcopenia 2 but also exemplify a major issue: disparate definitions impede the diagnosis and evidence-based treatment of sarcopenia.Resistance training is an effective intervention to combat age-related non-communicable disease, co-morbidities and loss of physical function, all of which impact older adults' ability to accomplish activities of daily living (e.g., feeding, bathing, dressing and toileting).3 All physical activity and exercise types benefit older adults; however, resistance training is most effective at improving muscle strength and power, augmenting (or mitigating loss of) muscle mass and improving physical function, all of which help maintain independence and improve their overall quality of life as they age.4 Consequently, experts have emphasized resistance training as a first-line strategy to prevent and manage sarcopenia.2,5 Recent evidence suggests that resistance training programmes consisting of two exercise sessions per week (upper-and lower-body exercises) performed with a relatively E D I T O R I A L
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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 | 0.149 | 0.321 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.009 | 0.029 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".