Need for epidemiological evidence from the developing world to know the cancer-related risk factors
Bibliographic record
Abstract
The existing evidence on cancer etiology has mostly come from epidemiological studies conducted in the developed world. Now there is an urgent need to gather information on cancer risks in developing countries. Due to recent economic, demographic and health transitions, cancers are on the rise in many developing countries. Future epidemiological studies in these countries should address changing diet, level of physical activity, various environmental and occupational exposures, smoking habits and infections, relative to cancers. In many low resource settings western and conventional lifestyles can be found side by side. Therefore, epidemiological studies in such societies should determine the wide varieties of potentially dangerous exposures, examine changing patterns of related factors and should study other contributing variables as well. Apart from the advantages of such research, there are some challenges. For example, incomplete cancer and death registration, lack of documentation, only partial computerization of medical records, cultural barriers and other technical difficulties can present problems. Some strategies to meet these challenges will be discussed in this paper. There is an immediate need for more detailed epidemiological studies before these developing societies are transformed.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".