Bibliographic record
Abstract
Introduction: sarcopenia is the generalized loss of muscle mass and can be defined by axial skeletal muscle area on cross-sectional imaging.It has been correlated with patient outcomes across many fields, including urologic oncology, and is regarded as an objective measurement of frailty in geriatric patients.sarcopenia has been linked to risk of nephrolithiasis; however, no studies to date have examined its utility in predicting perioperative risk in endourology.Accordingly, the objective of this study was to determine if sarcopenia can be used as a prognosticator of outcomes after percutaneous nephrolithotomy (pCnl) in a cohort of higher-risk elderly patients.Methods: patients ≥70 years with cardiovascular disease who underwent pCnl from 2014-2019 at our institution were identified retrospectively.patients without computed tomography (Ct) imaging within one year of surgery were excluded.sarcopenia was measured using skeletal muscle index (smI).smI was calculated by measuring total skeletal muscle (-29 to 150 Hounsfield Units) area at the mid-l3 vertebral body axial cut on Ct, divided by height squared.Image analysis was performed using Aquarius intuition software.smI <55 and <39 cm2/m2 was used to define sarcopenia for men and women, respectively.Results: the study cohort consisted of 80 patients, of which 56 (70%) met criteria for sarcopenia, with median smI 45.1 and 39.6 cm2/m2 for males and females, respectively.there was no difference in mean age between the groups, however, the prevalence of sarcopenia was significantly higher among men compared to women (73.2% vs. 26.8%,p<.0001).there were no significant differences in transfusion rate (7.1% vs. 12.5%), ed readmissions (21.4% vs. 25%), or postoperative complications (28.6% vs. 37.5%) between sarcopenic and non-sarcopenic patients.length of stay was similar between groups.Conclusions: this is a novel evaluation of sarcopenia as a predictor for outcomes after pCnl in higher-risk elderly patients.While sarcopenia was highly prevalent in our study cohort, it was not associated with increased perioperative complications and was not a predictor for inferior outcomes.Further studies focusing on a more general cohort are warranted and may have value in evaluating how sarcopenia can be used to guide surgical decision-making.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.236 | 0.097 |
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".