A Meta-Analysis of Risk Factors for Liver Cirrhosis Combined with Upper Gastrointestinal Bleeding in China
Bibliographic record
Abstract
In order to systematically evaluate the risk factors for liver cirrhosis complicated with upper gastrointestinal bleeding in China, we searched CNKI, Wanfang Digital Journals Full-text Database (Wanfang), Weipu Journal Resource Integration Service Platform(VIP) and PubMed database on risk factors for liver cirrhosis combined with upper gastrointestinal bleeding in China, Newcastle-Ottawa Scale(NOS) used the most comprehensive data collection based on relevant case-control trials to evaluate the quality of the extracted literature combined with inclusion and exclusion criteria. Studies with a score of ≥ 7 were included and meta-analysed using RevMan 5.4. Finally, twenty-one articles met the inclusion criteria, the cumulative number of cases and controls were 2222 and 2785 cases. We can come to the conclusion that Spleen, gastric varices, esophageal varices, PT (prothrombin time), ascites, left gastric vein diameter, liver function child grade C, esophageal varices, liver cirrhosis, portal vein diameter, and alcohol consumptionIt is an independent risk factor for liver cirrhosis combined with upper gastrointestinal bleeding in China, and the control of the above factors can effectively improve the risk of patients with liver cirrhosis and upper gastrointestinal bleeding in China.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.040 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".