Systematic Assessment of Risk of Fever in Solid Tumor Patients Treated With PD-1/PD-L1 Inhibitors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Purpose Drug-induced fever is frequently reported in cancer patients treated with anti-programmed death-1 (PD-1) ligand 1 (PD-L1), and stoppage of the offending agent is the management of choice. However, given the complex management of cancer patients, this needs to be carefully studied. Therefore, we conducted a meta-analysis to estimate the risk of fever associated with anti-PD-1/PD-L1 in cancer patients. Method: From May 2010 to 2020, an electronic search was conducted through PubMed for relevant studies. All clinical trials reporting fever in cancer patients treated with PD-1/PD-L1 inhibitors were included, while other designs were excluded. A manual search was also conducted to search for relevant articles. Outcomes included the risk of pyrexia and febrile neutropenia in the overall population and based on the grade of fever (all-grade vs. grade 3-5). The Newcastle Ottawa Scale was used to assess the quality of included studies. Results: Thirty-one articles, involving 27 clinical trials and 15867 participants, were included. The increased risk of pyrexia for all-grade is only found when PD-1/PD-L1 plus CTLA-4 was compared to CTLA-4 (OR=2.48, 95%CI: [1.17, 5.23]). The risk of febrile neutropenia for all-grade fever was significantly lower in the PD-1/PD-L1 group compared to chemotherapy alone (OR=0.02, 95%CI: [0.01, 0.05]). A similar trend in the risk of febrile neutropenia was also found for grades 3-5 (OR=0.02, 95%CI: [0.01, 0.05]). Conclusion: The increased risk of pyrexia for all-grades could only be found when PD-1/PD-L1 plus CTLA-4 was compared with CTLA-4. Meanwhile, compared to chemotherapy, PD-1/PD-L1 inhibitors reduced the risk of febrile neutropenia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.023 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".