Cancer in the world: a call for international collaboration
Bibliographic record
Abstract
Cancer in the worldArtículo especiAl C ancer is a major health problem in the world.[1][2][3] Since the start of the 21 st century, cancer killed more people than died in World War II.This year, it is expected that there will be 12 million new cancer cases diagnosed and close to 8 million will die of cancer.This year it is expected that 1.4 million people will die from lung cancer with 866 000 from stomach cancer, 653 000 from liver cancer, 677 000 from colon cancer and over half a million, 548 000 deaths will be due to breast cancer.Today, a new breast cancer case in diagnosed in the world approximately every 25 seconds.It is estimated that by the year 2030, 12 million people will die each year if we do not act today and improve cancer control.At the same time our knowledge about cancer has never been greater.These statistics are a call for action.International collaboration across all sectors is needed to improve cancer control and reverse the trend.Cancer arises from a change in one single cell and that change may be started by external agents and inherited genetic factors.Today, it is known that tobacco is the single most important external agent causing cancer.4 Almost 70% of all deaths in the world from cancer occur in the low-and middle-income countries.1 It is expected that 43% of cancer deaths be due to tobacco, poor diet, and infection.While over 40% of all cancers in the Western world are due to tobacco consumption and poor Cancer in the world -a call for international collaboration
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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 | 0.036 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.006 | 0.026 |
| Research integrity | 0.020 | 0.037 |
| Insufficient payload (model declined to judge) | 0.056 | 0.015 |
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".