India must use research evidence to tackle high burden of cervical cancer, experts say
Bibliographic record
Abstract
India needs immediately to translate its science research findings into effective policies to reduce the country’s huge burden of cervical cancer, researchers have urged.1 Cervical cancer is the most common cancer in women aged 15 or older in India. One in 53 Indian women in their lifetime develops cervical cancer, double the one in 100 in developed countries.2 India carries a quarter of the global burden of cervical cancer, and it is projected that at the current incidence India’s annual burden of new cases of cervical cancer would be 225 000 by 2025. “We are recommending implementation of …
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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 | 0.046 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.020 | 0.037 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".