Sarcopenia - Agreement and association with falls
Bibliographic record
Abstract
Objectives: Sarcopenia is defined using a variety of different muscle variables, muscle mass adjustment techniques and cut offs for each variable. The objectives of this thesis were to assess how operational differences in sarcopenia definitions impact the agreement between definitions and the association between sarcopenia and health outcomes such as falls. Methods: A list of sarcopenia definitions was developed which captured the combinations of muscle variables, muscle mass adjustment techniques, and cut offs used in the literature based on a systematic review conducted for this thesis. These definitions were applied to participants taking part in the Canadian Longitudinal Study on Aging, a national study of participants aged 45 to 85 years at baseline. The agreement between the definitions and the association of each definition with falls was assessed. Findings: Both the combination of muscle variables as well as the different muscle mass adjustment techniques generally had limited agreement. Sarcopenia definitions including muscle mass and muscle strength were associated with falls in males, but none of the sarcopenia definitions were associated with falls in females. Area under the curve analyses revealed that even sarcopenia definitions associated with more than two times the odds of falling in males, had a small impact on identifying fallers with values ≤0.56. Conclusions: The results of this thesis show that the existing range of definitions used to define sarcopenia are not equivalent based on the limited agreement and inconsistent association of sarcopenia with falls. The results also show that sarcopenia may have limitations as clinically useful diagnosis for identifying fallers with area under the curve values for all definitions showing that the identification of fallers based on sarcopenic status was at best, modestly better than chance alone.
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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 | 0.055 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".