The Impact of Quadriceps Muscle Layer Thickness on Length of Stay of Patients Listed for Renal Transplant
Bibliographic record
Abstract
Background: Quadriceps muscle layer thickness (QMLT), which is measured using ultrasound, is an emerging strategy to identify sarcopenia. Purpose: The purpose of the study was to assess whether pre-operative QMLT values are associated with a prolonged length of stay (LOS; defined as >14 days) following a renal transplant. Methods: Between March 2019 and January 2020, we performed a prospective study among patients undergoing renal transplantation. Physical Frailty scores and QMLT measurements were performed pre-operatively. The primary outcome was a greater LOS following transplant. Secondary outcomes included complications and renal function. Statistical analysis: Percentiles divided patients into two categories of QMLT (low and high). Continuous outcomes were compared using a two-sided t-test or Mann–Whitney U test, and Chi-square analysis and Fisher exact testing were used for nominal variables. Results: Of 79 patients, the frailty prevalence was 16%. Among patients with low and higher QMLTs, LOS of >14 days were 21% vs. 3% [p = 0.04], respectively. Demographically, there was a higher percentage of patients with living donors in the high- vs. low-QMLT groups (40 vs. 7%). However, in a subgroup analysis excluding living-donor recipients, the difference between groups was preserved (23% vs. 0%, p = 0.01). No differences in secondary outcomes were seen between groups. Conclusions: Low quadriceps muscle layer thickness may be associated with a prolonged length of stay for renal recipients. Further research is needed to confirm our findings.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".