Low calf circumference adjusted for body mass index is associated with prolonged hospital stay
Bibliographic record
Abstract
BACKGROUND: Calf circumference (CC) is of emerging importance because of its practicality, high correlation with skeletal muscle, and potential predictive value for adverse outcomes. However, the accuracy of CC is influenced by adiposity. CC adjusted for BMI (BMI-adjusted CC) has been proposed to counteract this problem. However, its accuracy to predict outcomes is unknown. OBJECTIVES: To evaluate the predictive validity of BMI-adjusted CC in hospital settings. METHODS: ) of 25-29.9, 30-39.9, and ≥40, respectively. Low CC was defined as ≤34 cm for males and ≤33 cm for females. Primary outcomes included length of hospital stay (LOS) and in-hospital death, and secondary outcomes were hospital readmissions and mortality within 6 mo after discharge. RESULTS: We included 554 patients (55.2 ± 14.9 y, 52.9% men). Among them, 25.3% presented with low CC, whereas 60.6% had BMI-adjusted low CC. In-hospital death occurred in 13 patients (2.3%), and median LOS was 10.0 (5.0-18.0) d. Within 6 mo from discharge, 43 patients (8.2%) died, and 178 (34.0%) were readmitted to the hospital. BMI-adjusted low CC was an independent predictor of LOS ≥ 10 d (odds ratio = 1.70; 95% confidence interval: 1.18, 2.43], but it was not associated with the other outcomes. CONCLUSIONS: BMI-adjusted low CC was identified in more than 60% of hospitalized patients and was an independent predictor of longer LOS.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".