Selection of second-line drugs for adult autoimmune hepatitis: A Meta-analysis
Bibliographic record
Abstract
ObjectiveTo investigate the clinical effect and safety of second-line drugs in the treatment of adult patients with autoimmune hepatitis (AIH) through a Meta-analysis of related articles. MethodsPubMed, EMBASE, Cochrane Library, Web of Science, CNKI, CBM, Wanfang Data, and VIP were searched for the articles on second-line drugs for AIH published up to December 31, 2019. The Newcastle-Ottawa Scale (NOS) was used to assess the quality of articles, related data were extracted, and Meta-Analyst software was used to perform the Meta-analysis. ResultsA total of 22 articles were included, with 636 patients in total. The Meta-analysis showed that mycophenolate mofetil (MMF), tacrolimus (TAC), cyclosporine, and budesonide had a pooled response rate of 56.1%, 76.0%, 62.7%, and 57.3%, respectively, and their pooled incidence rates of adverse events were 22.5%, 47.4%, 48.4%, and 33.0%, respectively. The patients treated with MMF were divided into groups based on intolerance or no response to the first-line treatment, and a comparative analysis of these groups showed a relative risk of 1.965 (95% confidence interval: 1.181-3.269, I2=0.665, P=0.014). ConclusionSelection of second-line drugs for adult AIH patients should consider response rate and incidence rate of adverse reactions. Both MMF and tacrolimus are good second-line drugs, and TAC may be a better choice for patients with no response to first-line treatment.
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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.070 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 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".