A Systematic Review and Meta-Analysis of Injection Site Reactions in Randomized-Controlled Trials of Biologic Injections
Bibliographic record
Abstract
BACKGROUND: Biologic agents are emerging as an important treatment option for immune-mediated diseases. Injection site reactions following subcutaneous injection of biologic agents is not well described in the literature. OBJECTIVE: To summarize injection site reaction data in phase 3 trials of all biologic agents. METHODS: MEDLINE, Embase, and CENTRAL databases were systematically searched on February 8, 2022. Proportional meta-analysis was conducted to summarize injection site reaction prevalence for each biologic. RESULTS: There were 158 articles included in the review. The most common types of injection site reactions were erythema (42.8%), unspecified reaction (23.3%), pain (12.4%), and pruritus (5.7%). No patients discontinued their treatment due to injection site reactions in 39 of the 48 studies that reported on discontinuation data. There were 16 biologics included in meta-analysis across 80 eligible studies. The biologics with the highest point prevalence of patients reporting injection site reactions were Canakinumab (15.5%; 294 patients), Dupilumab (11.4%; 1888 patients), Etanercept (11.4%; 4363 patients), and Ixekizumab (11.2%; 2205 patients). The biologics with the lowest point prevalence of injection site reactions were Risankizumab (0.8%; 707 patients), Brodalumab (1.3%; 1365 patients), Guselkumab (1.3%; 1852 patients), Secukinumab (1.9%; 1277 patients). CONCLUSIONS: The prevalence of injection site reaction in response to biologics ranges from 0.08 to 15.5%. Canakinumab, Dupilumab, Etanercept, and Ixekizumab had the highest prevalence of injection site reactions. Risankizumab, Brodalumab, Guselkumab, and Secukinumab had the lowest prevalence of injection site reactions. Recommendations are made regarding the improvement of adverse event reporting to better understand the epidemiology of injection site reactions.
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 | 0.031 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.044 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".