Bibliographic record
Abstract
Liver cancer (LC) ranks fifth in frequency in the world, with an estimated 437,000 new cases in 1990. The estimates are different when LC frequency is analyzed by sex and geographical areas. In developed areas, the estimates are 53,879 among men and 26,939 among women. In developing areas, the estimates are 262,043 in men and 93, 961 in women. Areas of highest rates include Eastern and South Eastern Asia, Japan, Africa and the Pacific Islands (LC age-adjusted incidence rates [AAIRs] ranging from 17.6 to 34.8). Intermediate rates (LC AAIRs from 4.7 to 8.9 among men) are found in Southern, Eastern and Western Europe, Central America, Western Asia and Northern Africa. Low rates are found among men in Northern Europe, America, Canada, South Central Asia, Australia and New Zealand (LC AAIRs range from 2.7 to 3.2). In Europe, an excess of LC incidence among men compared with women is observed, and the age peak of the male excess is around 60 to 70 years of age. Significant variations in LC incidence among different countries have been described and suggest differences in exposure to risk factors. Chronic infection with the hepatitis B virus (HBV) and hepatitis C virus (HCV) in the etiology of LC is well established. In Europe, 28% of LC cases have been attributed to chronic HBV infection and 21% to HCV infection. Other risk factors such as alcohol consumption, cigarette smoking and oral contraceptives may explain the residual variation within countries. Interactions among these risk factors have been postulated. New laboratory techniques and biological markers such as polymerase chain reaction detection of HBV DNA and HCV RNA, as well as specific mutations related to LC, may help to provide quantitative estimates of the risk related to each these factors.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".