Prospects for controlling cervical cancer at the turn of the century
Bibliographic record
Abstract
Cervical cancer morbidity and mortality have decreased substantially during the last 50 years mostly due to successful organized or opportunistic screening with Pap cytology in high and middle income countries. In many low income countries Pap cytology screening is yet to be effectively implemented or has failed to reduce cervical cancer rates to an appreciable extent. The fact that infection with certain human papillomavirus (HPV) types is now recognized as a necessary cause of this disease has led to new research fronts on prevention of cervical cancer. Testing for HPV DNA has shown great promise as a screening tool with better sensitivity but somewhat lower specificity than Pap cytology. In combination with the latter, HPV testing has the potential to improve the negative predictive value of cytology, thus allowing for increased testing intervals, which would lower program costs with acceptable safety. Advances in cytology processing and automation have also led to new screening approaches that are increasingly gaining acceptance in high and middle income countries. For low income countries, visual inspection with acetic acid has proven to be an effective alternative to conventional Pap cytology, especially in settings where no screening programs have been implemented. Concerning primary prevention of cervical cancer, recent research on the safety and efficacy of candidate prophylactic vaccines against HPV have shown very promising results with nearly 100% efficacy in preventing persistent infections and development of cervical cancer precursors. However, policy makers are strongly cautioned to avoid deferring decisions concerning the implementation of cervical cancer screening under the expectation that a successful vaccine could obviate the need for secondary prevention strategies. This paper is available too at: http://www.insp.mx/salud/index.html.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.011 |
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".