A systematic analysis of multiple myeloma and the related risk of HBV and HCV infection
Bibliographic record
Abstract
Objective This study aimed to assess the risk of hepatitis B virus (HBV) and hepatitis C virus (HCV) in patients with multiple myeloma (MM) by systematic analysis. Methods The literature was retrieved in 4 English databases (PubMed, Web of science, OVID, Cochrane Library) and 3 Chinese databases (CNKI, VIP, and Wanfang) as of september 2016. The key word in English/ Chinese database retrieval is MM or multiple myeloma and infection . Newcastle Ottawa Scale (NOS) was used to evaluate the article quality. The overall odds ratio (OR) and 95% confidence intervals (95% CIs) were estimated by fixed (heterogeneity test at I2<25%) or random (heterogeneity test at I2≥25%) effects model. Results A total of 9 studies were included in the Meta analysis. The study found that multiple myeloma patients had an increased risk of HBV and HCV infection, 3.17 and 4.16 times higher than the control group. HBV (OR=3.17, 95% CI: 1.17-1.96, I2=27%, P=0.002) and HCV (OR=4.16, 95% CI: 1.33-2.22, I2=0%, P<0.001) in the multiple myeloma group were significantly different from those of the control group (P<0.05). Conclusions HBV and HCV are risk factors for multiple myeloma and should be highly regarded and controlled before and after treatment. Key words: Multiple myeloma/CO; Hepatitis B/CO; Hepatitis C/CO; Meta-analysis
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".