An in-depth analysis on the effects of body composition in patients receiving neoadjuvant chemotherapy for urothelial cell carcinoma
Bibliographic record
Abstract
INTRODUCTION: Neoadjuvant chemotherapy (NAC) is the standard of care for patients undergoing radical cystectomy (RC) for muscle-invasive bladder cancer (MIBC); however, NAC can be associated with significant side effects and morbidity in some patients. NAC may contribute to sarcopenia, obesity, and the combination of the two. Our study examined the effects of NAC on body composition and the association between body composition and adverse events. METHODS: We created a retrospective database of patients with non-metastatic MIBC receiving NAC prior to RC. The change in skeletal muscle index (SMI) and fat mass index (FMI) was calculated using computed tomography (CT) scans done within three months prior to NAC and after the first two cycles. The association between body composition (sarcopenia, obesity, and sarcopenic obesity) and preoperative adverse events was investigated using a multivariable logistic regression. Changes in body composition were calculated using a paired Student's t-test. RESULTS: . Adiposity and FMI were unchanged by NAC. Sarcopenic obesity was found to be associated with adverse events among patients receiving NAC in the multivariable analysis. There was a total of 637 preoperative complications with grades 1-2 and 33 complications with grades 3-5. CONCLUSIONS: Based on our retrospective cohort study, NAC did not affect obesity and FMI, but there was a significant decrease in SMI. Sarcopenic obesity was associated with increased severity of NAC adverse events. As such, the presence of this factor may help predict tolerance of NAC.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".