High-burden Cancers in Middle-income Countries: A Review of Prevention and Early Detection Strategies Targeting At-risk Populations
Bibliographic record
Abstract
Cancer incidence is rising in low- and especially middle-income countries (MIC), driven primarily by four high-burden cancers (breast, cervix, lung, colorectal). By 2030, more than two-thirds of all cancer deaths will occur in MICs. Prevention and early detection are required alongside efforts to improve access to cancer treatment. Successful strategies for decreasing cancer mortality in high-income countries are not always effective, feasible or affordable in other countries. In this review, we evaluate strategies for prevention and early detection of breast, cervix, lung, and colorectal cancers, focusing on modifiable risk factors and high-risk subpopulations. Tobacco taxation, human papilloma virus vaccination, cervical cancer screen-and-treat strategies, and efforts to reduce patient and health system-related delays in the early detection of breast and colorectal cancer represent the highest yield strategies for advancing cancer control in many MICs. An initial focus on high-risk populations is appropriate, with increasing population coverage as resources allow. These strategies can deliver significant cancer mortality gains, and serve as a foundation from which countries can develop comprehensive cancer control programs. Investment in national cancer surveillance infrastructure is needed; the absence of national cancer data to identify at-risk groups remains a barrier to the development of context-specific cancer control strategies.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".