Nivolumab Hypersensitivity Reactions a Myth or Reality in Solid Tumors—A Systematic Review of the Literature
Bibliographic record
Abstract
Immune-checkpoint inhibitors (ICIs) are the most effective treatments nowadays. Nivolumab was the second ICI used for treating solid tumors with amazing results. Patients treated with Nivolumab may react differently to this treatment. Some people tolerate this treatment very well without experiencing any adverse reactions, whilst some may have mild symptoms and a part of them can present severe reactions. In our research, we sought to identify the answers to four questions: 1. what type of cancer has more severe hypersensitivity reactions to Nivolumab, 2. what is the time frame for developing these severe reactions to Nivolumab, 3. whether it is best to continue or stop the treatment after a severe hypersensitivity reaction to Nivolumab and 4. what severe hypersensitivity reactions are the most frequent reported along Nivolumab treatment. This review also highlights another problem with regard to the usage of concomitant and prior medications or other methods of treatment (e.g., radiation therapy), which can also lead to severe reactions. Treatment with Nivolumab is very well tolerated, but patients should also be warned of the possibility of severe hypersensitivity reactions for which they should urgently see a doctor for a personalized evaluation. There are some options for individuals with severe hypersensitivity reactions, for eg. switching the medication or applying a desensitization protocol.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".