Prognostic impact of sarcopenia on immune-related adverse events in malignancies received immune checkpoint inhibitors: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Whether sarcopenia has an impact on immune-related adverse events (irAEs) in patients with malignant neoplasms receiving immune checkpoint inhibitors (ICIs) is not consistent. This study aimed to evaluate the impact of sarcopenia on all grades of irAEs. Methods: PubMed, Embase, and Cochrane Library databases were systematically searched for related studies up to May 2021. Eligible studies were included according to the PICOS criteria. The risk of bias of the included studies was assessed according to the Newcastle-Ottawa Scale (NOS). The odds ratio (OR), corresponding to the 95% confidence interval (CI) of all grades of irAEs, was collected and analyzed, and a further subgroup analysis of serious adverse events was conducted. All analyses were conducted using the RevMan 5.4 software downloaded from the Cochrane website. The heterogeneity and sensitivity of the study were assessed. Results: Of the 135 references identified, only 8 studies were analyzed, including 519 patients comprising 250 with sarcopenia and 269 without sarcopenia. No obvious bias was observed in the included studies. An increased incidence of irAEs was not observed in patients with sarcopenia at pre-immunotherapy compared to those without sarcopenia. The OR and corresponding 95% CI were 0.97 and 0.62–1.53, respectively (P=0.90), with low heterogeneity (P=0.17, I2 =32%). Further, severe adverse events were analyzed in three studies, and the results showed that sarcopenia was not related to irAEs (P=0.97). Conclusions: Malignancies with sarcopenia at pre-immunotherapy may not increase the incidence of irAEs, and sarcopenia may not be a predictive factor for irAEs.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.041 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".