Clinical Efficacy of Infliximab in Patients With Crohn Disease in Different Locations of Disease Pathology: A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Infliximab (INX) has been approved for treating Crohn disease (CD) for many years, showing promis-ing efficacy in the clinic. However, the efficacy of the drug and the prognosis of CD vary significantly with dif-ferent locations of disease pathology. This study evaluated the efficacy of INX and prognosis in CD in different locations of disease pathology using systematic meta-analysis. METHODS: We used "Infliximab OR Remicade OR Avakine OR Inflectra OR Renflexis OR Remsima OR IgG1k monoclonal antibody" AND "Crohn's disease OR IBD OR inflammatory bowel disease" as search strategies for searching in PubMed, Wanfang and Embase. A systematic meta-analysis for overall proportions was used to analyze the data. RESULTS: Twelve studies involving 1,978 patients were included. The results confirmed that treatment with INX led to high clinical remission rates (82%, 95% CI: 64%-92%) and low relapse rates (4%, 95% CI: 2%-9%) in patients with CD. Our results also indicated that use of INX in patients with colon only (L2) CD led to lower clinical remission rates, and use of INX in patients with ileum and colon (L3) CD led to higher relapse rates. CONCLUSION: Our findings show different remission rates depending on location of the disease and may be useful for clinicians' choice of therapeutics.
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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.046 |
| Bibliometrics | 0.005 | 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 | 0.002 |
| 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".