RESEARCH ARTICLE Prevalence and risk factor r
Bibliographic record
Abstract
Full list of author information is available at the end of the articleIntroduction Globally, cervical cancer is the third most common female cancer and cause of female cancer-related deaths, with an estimated 530,000 cases and 265,653 deaths annually, respectively [1]. In low- and middle-income countries (LMICs), cervical cancer is often the most common cancer, the leading cause of cancer-related mortality, and a leading cause of all-cause mortality in women due to a lack of preventive services. Cervical cancer constitutes 13 % of all female cancers in LMICs [2,3]. More than 85% of cervical cancer deaths occur in LMICs [3]. Approxi-mately one quarter of cervical cancers and related mortal-ity globally occur in Africa, and>90 % of those occur in Sub-Saharan Africa, which has an overall age-standardized rate of cervical cancer incidence of 34.8 per 100,000.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.034 | 0.007 |
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".