Musculoskeletal immune-related adverse events of PD-(L)1 inhibitors in melanoma: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) are first-line treatment for melanoma. The incidence of musculoskeletal immune-related adverse events (MSK irAEs) remains unclear. OBJECTIVE: To estimate the relative risk of MSK irAEs in melanoma patients treated with ICIs targeting programmed cell death-1 or its ligand PD-(L)1 as compared to placebo. METHODS: We performed a systematic literature review including phase III randomized controlled trials of adult melanoma patients comparing a PD-(L)1 inhibitor to a placebo arm. Outcomes of interest included arthralgias, arthritis, back pain and myalgias. Meta-analysis was performed to estimate the pooled relative risk of MSK irAEs over the treatment course. RESULTS: = 3,041 subjects). Use of PD-(L)1 inhibitors was associated with an increased risk of developing arthralgias (RR 1.30 [95% CI: 1.13-1.49]) and myalgias (RR 1.48 [95% CI: 1.17-1.87]) as compared to placebo. Back pain and arthritis were not reported. CONCLUSIONS: Use of PD-(L)1 inhibitors is associated with a significantly increased risk of arthralgias and myalgias in melanoma patients. The risk of back pain and arthritis is unknown. IMPLICATIONS FOR PRACTICE: MSK irAEs can impact quality of life and should be considered, particularly in the adjuvant setting when risks and benefits are carefully weighed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".