Gastrointestinal and liver tumours and public health in Europe.
Bibliographic record
Abstract
STATE OF THE ART: Europe comprises only one eighth of the total world population but has around one quarter of the global total of cancer cases--some 3.2 million new patients per year. While the disproportionate cancer burden is readily apparent, the disease patterns in Europe cannot simply be generalized--overall cancer incidence and mortality rates vary at least two-fold between European countries and the differences are often far greater for specific cancers. With 1.7 million deaths each year, cancer currently represents the second most important cause of death in Europe. The range of survival rates is similarly wide. For individual cancers, the variation across Europe is even greater. This reflects a wide range of social and epidemiological factors in different countries: cancer prevention programmes; screening programmes; cancer control plans; individual lifestyles and occupational exposures; the existence and accessibility of health-care facilities and technological infrastructure; and the availability of human, financial and material resources for health and economic development. Europe has some of the richest countries in the world, but also some of the poorest. In 2002, 168 million people were living below the poverty line, about 46% of the European population. The time trends in cancer risk also vary between European countries and some cancers show different trends between men and women, or young and old, or poor and rich. The public health profile of cancer in Europe is complex. Trends in the incidence and mortality rates are also influenced by successes in health promotion (e.g. tobacco control), efficient screening (e.g. breast, bowel, cervix) and better treatment. These have been reflected in lower incidence, reduced mortality, higher survival, improved life expectancy and a better quality of life for cancer survivors. CONCLUSIONS: Cancer of the gastrointestinal (GI) tract is the most common cancer in Europe. More than half of GI cancer cases arise from the colon. They can remain asymptomatic until late in the natural history of the disease, and as this is the stage at which they can be cured, screening has been advocated for well members of the population and surveillance for those with conditions predisposing to cancer.
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 | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".