The role and utility of population-based cancer registries in cervical cancer surveillance and control
Bibliographic record
Abstract
Population-based cancer registries (PBCR) are vital to the assessment of the cancer burden and in monitoring and evaluating national progress in cervical cancer surveillance and control. Yet the level of their development in countries exhibiting the highest cervical cancer incidence rates is suboptimal, and requires considerable investment if they are to document the impact of WHO global initiative to eliminate cervical cancer as a public health problem. As a starting point we propose a comprehensive cancer surveillance framework, positioning PBCR in relation to other health information systems that are required across the cancer control continuum. The key concepts of PBCR are revisited and their role in providing a situation analysis of the scale and profile of the cancer-specific incidence and survival, and their evolution over time is illustrated with specific examples. Linking cervical cancer data to screening and immunization information systems enables the development of a comprehensive set of measures capable of assessing the short- and long-term achievements and impact of the initiative. Such data form the basis of national and global estimates of the cancer burden and permit comparisons across countries, facilitating decision-making or triggering cancer control action. The initiation and sustainable development of PBCR must be an early step in the scale-up of cervical cancer control activities as a means to ensure progress is successfully monitored and appraised.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.236 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".