Lower limb muscle matters in patients with hypoxic brain injury following out-of-hospital cardiac arrest
Bibliographic record
Abstract
BACKGROUND: There are conflicting results regarding the association between body mass index and the prognosis of cardiac arrest patients. We investigated the association of the composition and distribution of muscle and fat with neurologic outcomes at hospital discharge in successfully resuscitated out-of-hospital cardiac arrest (OHCA) patients. METHODS: This prospective, single-centre, observational study involved adult OHCA patients, conducted between April 2019 and June 2021. The ratio of total skeletal muscle, upper limb muscle, lower limb muscle, and total fat to body weight was measured using InBody S10, a bioimpedance analyser, after achieving the return of spontaneous circulation. Restricted cubic spline curves with four knots were used to examine the relationship between total skeletal muscle, upper limb muscle, and lower limb muscle relative to total body weight and neurologic outcome at discharge. Multivariable logistic regression analysis was performed to assess an independent association. RESULTS: A total of 66 patients were enrolled in the study. The proportion of total muscle and lower limb muscle positively correlated with the possibility of having a good neurologic outcome. The proportion of lower limb muscle showed an independent association in the multivariable analysis (adjusted odds ratio, 2.29; 95% confidence interval, 1.06-13.98), and its optimal cut-off value calculated through receiver operating characteristic curve analysis was 23.1%, which can predict a good neurological outcome. CONCLUSIONS: A higher proportion of lower limb muscle to body weight was independently associated with the probability of having a good neurologic outcome in OHCA patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".