Systematic review with meta‐analysis: COVID‐19 outcomes in patients receiving anti‐TNF treatments
Bibliographic record
Abstract
Summary Background Accumulating evidence suggests a beneficial effective of tumour necrosis factor‐alpha (TNF‐α) inhibitors on the outcomes of COVID‐19 disease, which, however is not validated by all studies. Aims To perform a systematic review and meta‐analysis of existing reports to investigate the impact of anti‐TNF treatments on the clinical outcomes of COVID‐19 patients. Methods A systematic search at PubMed and SCOPUS databases using specific keywords was performed. All reports of COVID‐19 outcomes for patients receiving anti‐TNF therapy by September 2021 were included. Pooled effect measures were calculated using a random‐effects model. The Newcastle Ottawa Scale for observational studies was used to assess bias. Studies that were not eligible for meta‐analysis were described qualitatively. Results In total, 84 studies were included in the systematic review, and 35 were included in the meta‐analysis. Patients receiving anti‐TNF treatment, compared to non‐anti‐TNF, among COVID‐19 cases had a lower probability of hospitalisation (eight studies, 2555 patients, pooled OR = 0.53, 95% CI: 0.42‐0.67, I 2 = 0) and severe disease defined as intensive care unit admission or death (two studies, 1823 patients, pooled OR = 0.63, 95% CI: 0.41‐0.96, I 2 = 0), after adjustment for validated predictors of adverse disease outcomes. No difference was found for the risk for hospitalisation due to COVID‐19 in populations without COVID‐19 for patients receiving anti‐TNF treatment compared to non‐anti‐TNF (three studies, 5 994 958 participants, pooled risk ratio = 0.97, 95% CI: 0.68‐1.39, I 2 = 20) adjusted for age, sex and comorbidities. Conclusions TNF‐α inhibitors are associated with a lower probability of hospitalisation and severe COVID‐19 when compared to any other treatment for an underlying inflammatory disease.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".