Association of Temporalis Muscle Mass with Early Cognitive Impairment in Older Patients with Acute Ischemic Stroke
Bibliographic record
Abstract
The prognostic value of temporal muscle mass has been studied in various neurological disorders. Herein, we investigated the association between temporal muscle mass and early cognitive function in patients with acute ischemic stroke. This study included 126 patients with acute cerebral infarction aged ≥65 years. Temporal muscle thickness (TMT) was measured using T2-weighted brain magnetic resonance imaging at admission for acute stroke. Within 2 weeks of stroke onset, skeletal mass index (SMI) and cognitive function were assessed using bioelectrical impedance analysis and the Korean version of the Montreal Cognitive Assessment (MoCA), respectively. Pearson’s correlation analyzed the correlation between TMT and SMI, and multiple linear regression analyzed independent predictors of early post-stroke cognitive function. TMT and SMI were significantly positively correlated (R = 0.36, p < 0.001). After adjusting for covariates, TMT was an independent predictor of early post-stroke cognitive function, stratified by the MoCA score (β = 1.040, p = 0.017), age (β = −0.27, p = 0.006), stroke severity (β = −0.298, p = 0.007), and education level (β = 0.38, p = 0.008). TMT may be used as a surrogate marker for evaluating skeletal muscle mass because it is significantly associated with post-stroke cognitive function during the acute phase of ischemic stroke; therefore, TMT may help detect older patients at a high risk of early post-stroke cognitive impairment.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".