Assessing the Effects of Immune Checkpoint Inhibitors and Pretreatment Kidney Function on Muscle Mass and Density
Bibliographic record
Abstract
Background: The association between skeletal muscle wasting and immune checkpoint inhibitor (ICI) use is unknown. Chronic kidney disease (CKD) is associated with sarcopenia. The relationship between pre-treatment kidney function on change in muscle mass and density while on immunotherapy is unknown. Methods: Single center retrospective cohort study of patients with melanoma with both a baseline CT or PET-CT scan and a follow-up scan within one year (± three months). ICI users were defined as patients with stage 3 melanoma who received at least 6 months of ICI therapy. The control group, referred to as non-ICI users, consisted of patients with stage 2 melanoma who had a baseline CT or PET-CT but did not receive any ICI therapy or chemotherapy prior to their follow-up scan. Paired t tests were used to examine the change in psoas muscle cross sectional area (CSA, cm2) and psoas muscle density [PMD, Hounsfield Units (HU)] at L3 between baseline and follow-up. Independent t tests were used to determine whether there were differences in the mean change from baseline to follow-up between the non-ICI users and the ICI users. Pearson correlation was used to assess the relationship between baseline estimated glomerular filtration rate (eGFR, ml/min/1.73m2) and baseline muscle measurements. Results: The mean age ± SD of patients in the non-ICI group was of 66.4 ± 12.8 years. The mean age in the ICI group was 58.4 ± 15.4 years. 76.19% of the non-ICI users were male compared to 56.25% in the ICI users. Baseline eGFR for non-ICI users was 73 ± 18 and 85 ± 20 ml/min/1.73m2 for ICI users. Baseline eGFR did not correlate with baseline CSA or PMD. The mean decline in CSA over 1 year in ICI users was -3.14 ± 16.02 and -3.11 ± 7.39 in non-ICI users. The mean decline in PMD was -3.75 ± 11.33 HU in ICI users and -0.31 ± 8.67 HU in non-ICI users. Compared to baseline values, both ICI and non-ICI users had significant difference in CSA and PMD on follow up scans. The difference in CSA and PMD were not significantly different between groups. Conclusion: Over 1-year, skeletal muscle area and density declines in both ICI users and non-ICI users. This work will inform linear regression models accounting for co-variates to further assess our findings and whether baseline eGFR modifies these associations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".