Sarcopenia and Visceral Adiposity Are Not Independent Prognostic Markers for Extensive Disease of Small-Cell Lung Cancer: A Single-Centered Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Sarcopenia and visceral adiposity have been suggested to affect prognosis and treatment efficacy in various types of cancers. The aim of our study was to evaluate whether pretreatment sarcopenia and visceral adiposity are associated with prognosis in patients with extensive-disease small-cell lung cancer (ED-SCLC). METHODS: Between September 2007 and March 2018, 128 ED-SCLC patients received first-line and platinum-based chemotherapy at our hospital. Based on pretreatment body mass index (BMI), psoas muscle index (PMI), intramuscular adipose tissue content (IMAC) and visceral-to-subcutaneous fat ratio (VSR) at lumbar vertebra L3 level, we divided these patients into two groups, and then compared overall survival (OS) and progression-free survival (PFS). Adjusted by age, serum albumin, lactate dehydrogenase (LDH), clinical stage and performance status, we detected independent prognostic factors by multivariate Cox proportional hazard analyses. RESULTS: We did not find any significant differences in OS and PFS between two groups divided by BMI, PMI, IMAC and VSR. According to multivariate analyses, none of BMI, PMI, IMAC and VSR was an independent prognostic factor of OS and PFS. CONCLUSIONS: Neither pretreatment sarcopenia nor visceral adiposity is a prognostic marker of patients with ED-SCLC treated with standard regimen of platinum-based chemotherapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".