Cervical cancer control, priorities and new directions
Bibliographic record
Abstract
Cervical cancer is caused by infection with a range of high risk "oncogenic" human papillomavirus (HPV) types, and it is now accepted that >99% of cervical cancer is initiated by HPV infection. The estimated lifetime risk of cervical cancer is nevertheless relatively low (less than 1 in 20 for most community based studies). Although sensitivity and specificity of the available diagnostic techniques are suboptimal, screening for persistent HPV infection is effective in reducing the incidence of cervical cancer. Infection can be detected by molecular techniques or by cytological examination of exfoliated cervical cells. Persistent infection is the single best predictor of risk of cervical cancer. The latest findings of HPV and cervical cancer research need to be widely disseminated to the scientific and medical societies that are updating screening and management protocols, public health professionals, and to women and clinicians. This report reviews current evidence, clinical implications and directions for further research in the prevention, control and management of cervical cancer. We report the conclusions of the Experts' Meeting at the EUROGIN 2003 conference.
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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.018 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".