Sarcopenia Definition and Outcomes Consortium 2020 Definition: Association and Discriminatory Accuracy of Sarcopenia With Disability in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
BACKGROUND: Previous sarcopenia definitions have poor discriminatory accuracy for identifying people with/without relevant health outcomes, and poor agreement between methods of operationalizing sarcopenia criterion. The 2020 Sarcopenia Definitions and Outcomes Consortium (SDOC) definition recommends grip strength (absolute, or standardized to body mass index, total body fat, lean arm mass, or weight), and gait speed. The agreement between methods of operationalizing grip strength and discriminatory accuracy of the SDOC definition for health outcomes such as activities of daily living (ADL) disability is unknown. METHODS: Cross-sectional analyses of 27 924 Canadian Longitudinal Study on Aging participants aged 45-85 at baseline (2012-2015) stratified by sex. The associations of the SDOC definitions with ADL disability were assessed using logistic regression. Area under the curve (AUC) analyses were conducted to assess discriminatory accuracy. Agreement between methods of operationalizing grip strength was measured using Cohen's kappa. RESULTS: Sarcopenia was associated with 1.60 (1.42-1.80) to 5.80 (4.89-6.88) greater odds of ADL disability with AUC values between 0.60 and 0.81. Agreement between methods of operationalizing grip strength was between 0.10-0.80 for grip strength alone and 0.45-0.91 when combined with gait speed. CONCLUSIONS: The SDOC-suggested criteria of grip strength and gait speed are significantly associated with ADL disability and have high discriminatory accuracy. However, the agreement between methods of operationalizing grip strength tended to be modest, and AUC, sensitivity, and specificity differed depending on the definition. We suggest a single measure of grip strength be considered and age-stratified cutoff values to improve AUC values.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".