Advancing Cervical Cancer Prevention in India: Implementation Science Priorities
Bibliographic record
Abstract
Cervical cancer is the leading cause of cancer mortality in India, accounting for 17% of all cancer deaths among women aged 30 to 69 years. At current incidence rates, the annual burden of new cases in India is projected to increase to 225,000 by 2025, but there are few large-scale, organized cervical cancer prevention programs in the country. We conducted a review of the cervical cancer prevention research literature and programmatic experiences in India to summarize the current state of knowledge and practices and recommend research priorities to address the gap in services. We found that research and programs in India have demonstrated the feasibility and acceptability of cervical cancer prevention efforts and that screening strategies requiring minimal additional human resources and laboratory infrastructure can reduce morbidity and mortality. However, additional evidence generated through implementation science research is needed to ensure that cervical cancer prevention efforts have the desired impact and are cost-effective. Specifically, implementation science research is needed to understand individual- and community-level barriers to screening and diagnostic and treatment services; to improve health care worker performance; to strengthen links among screening, diagnosis, and treatment; and to determine optimal program design, outcomes, and costs. With a quarter of the global burden of cervical cancer in India, there is no better time than now to translate research findings to practice. Implementation science can help ensure that investments in cervical cancer prevention and control result in the greatest impact.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| 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".