Prevalence and predictive value of sarcopenia in hospitalized patients with ischemic colitis
Bibliographic record
Abstract
Ischemic colitis (IC) and sarcopenia are associated with aging and multiple comorbidities. We aimed to investigate the prevalence and predictive role of sarcopenia in patients with IC. We retrospectively analyzed 225 hospitalized patients (median age, 72 years; women, 67.1%; severe IC, 34.2%) who were diagnosed with IC between January 2007 and February 2022. Sarcopenia was defined as the skeletal muscle index at the third lumbar vertebra determined by computed tomography. It was present in 49.3% (n = 111) of the patients and was significantly associated with severe IC compared to those without sarcopenia (48.6% vs. 20.2%, P < 0.001). Sarcopenia was associated with extended hospitalization (median: 8 vs. 6 days, P < 0.001) and fasting periods (4 vs. 3 days, P = 0.004), as well as prolonged antibiotic use (9 vs. 7 days, P = 0.039). Sarcopenia was linked to a higher risk of surgery or mortality (9.0% vs. 0%, P = 0.001) and independently predicted this outcome (odds ratio [OR], 11.17; 95% confidence interval [CI], 1.24‒1467.65, P = 0.027). It was prevalent among hospitalized patients with IC, potentially indicating severe IC and a worse prognosis. This underscores the importance of meticulous monitoring, immediate medical intervention, and timely surgical consideration.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".