Sarcopenia prevalence and relationship to frailty by measurement method in patients with adult spinal deformity
Bibliographic record
Abstract
OBJECTIVE: There is no consensus on how to diagnose sarcopenia in patients with spine disorders, limiting one's understanding of the relationship among sarcopenia, frailty, and surgical outcomes. The authors characterized the baseline prevalence of sarcopenia in patients with adult spinal deformity (ASD) according to previously established methods. They then examined the intersection between sarcopenia and frailty. METHODS: This is a retrospective cross-sectional study of preoperative patients with ASD at a single tertiary care center. Muscle function was assessed via hand grip strength, gait speed, and the Timed Up and Go (TUG) test. Bioelectrical impedance analysis was used to determine the skeletal muscle index. Muscle imaging included both CT to determine the psoas muscle index and MRI to assess myosteatosis. Diagnostic thresholds for sarcopenia were taken from the Sarcopenia Definitions and Outcomes Consortium (SDOC) and European Working Group on Sarcopenia in Older People 2 (EWGSOP2) consensus guidelines. Frailty was assessed using the Edmonton Frail Scale (EFS) and the adult spinal deformity frailty index (ASD-FI). Fisher's exact test, Spearman's rank correlation, and UpSet plot analyses were used to compare sarcopenia rates between measurement methods. Correlations between sarcopenia measures and frailty scores were also tested. RESULTS: Between 2023 and 2024, 101 patients with ASD were evaluated for sarcopenia. The mean age was 66.0 years, and 66 patients (65.3%) were female. The percentage of patients meeting SDOC or EWGSOP2 cutoff criteria for sarcopenia based on grip strength, age-normalized grip strength, grip strength/BMI, gait speed, TUG test, or skeletal muscle index ranged from 0% to 74.2%. The distribution of patients meeting each criterion differed significantly for male and female patients (p < 0.0001 for both). Despite this, all functional and imaging-based measures of sarcopenia, except for gait speed, were significantly correlated with each other. Interestingly, many sarcopenic patients were not frail (69.8% per the EFS, 47.5% per the ASD-FI), but nearly all frail patients were sarcopenic (100% per the EFS, 82.2% per the ASD-FI). Sarcopenia measures were not significantly correlated with frailty scores with a few exceptions. CONCLUSIONS: The baseline prevalence of sarcopenia in patients with ASD varied widely according to the measurement method (0%-74.2%). Despite this, nearly all measures of sarcopenia were significantly correlated with each other. However, many sarcopenic patients were not frail, and sarcopenia measures largely did not correlate with frailty scores. These results highlight the need for a consensus criterion for sarcopenia in patients with spine disorders.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".