Safety of Biological Therapies in Elderly Inflammatory Bowel Diseases: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Aim: Newer biologics appeared safer in landmark clinical trials, but their safety is understudied in vulnerable populations. The aim of the present study was to perform a systematic review and meta-analysis to assess the safety of available biologicals in the elderly IBD population. Methods: We systematically searched PubMed/Medline and conference proceedings between 1 April 1969 and 1 June 2021 to identify eligible studies that examined the safety of biologics in elderly patients with IBD. Of the 2885 articles and 12 congress abstracts identified, 12 peer reviewed papers and 3 abstracts were included after independent evaluation by two reviewers. The identified studies collected safety data on anti-TNF, vedolizumab (VDZ) and ustekinumab (UST). Results: Rates of AE and infections were not different among the biologics (AE mean rate: 11.3 (CI 95% 9.9–12.7)/100 pts-years; p = 0.11, infection mean rate: 9.5 (CI 95% 8.4–10.6)/100 pts-years; p = 0.56) in elderly IBD patients on anti-TNF, VDZ or UST. Infusion/injection reaction rates were more common on anti-TNFs (mean rate: 2.51 (CI 95% 1.7–3.4/100 pts-years; p = 0.02). and malignancy rates were higher on VDZ/UST (mean rate: 2.14 (CI 95% 1.6–2.8)/100 pts-years; p = 0.01). Conclusions: Rates of AEs and infections were not different among biologicals. Infusion/injection reactions were more common on anti-TNFs. Current data are insufficient to suggest the sequencing of biologicals in elderly patients based on safety.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".