Cervical Cancer Prevention in the Human Papilloma Virus Vaccine Era
Bibliographic record
Abstract
Globally, cervical cancer is second only to breast cancer as the leading cause of cancer in women, with a global prevalence of 2.3 million. It is the third most common cause of female cancer-related mortality worldwide, and 82% of new cervical cancer cases occur in developing countries. As stated by WHO, "without screening programs, cervical cancer is detected too late and leads to death in almost all cases." However, even in Europe, the United States, and Canada, where most women have access to routine screening, approximately 30,000 women die each year. Infection with oncogenic types of HPV 16 and 18 is the most significant risk/causative factor in cervical cancer etiology, and worldwide HPV positivity in cervical carcinoma has been documented to be 99.7%. In 2006 Merck's quadrivalent vaccine was approved by FDA. It targets four HPV types (6, 11, 16, and 18) that are involved in cervical cancer, high and low grade squamous intraepithelial lesions, and anogenital warts. Results from combined Phase II/III studies show that the efficacy of vaccine was 95-100% against LGSIL and HGSIL related to HPV 16 and 18 and vaccine use led to a 99% reduction in the incidence of genital warts (related to HPV 6 and 11). Due to morbidity associated with infection with HPV types 6, 11, 16, and 18, a prophylactic quadrivalent HPV vaccine targeting these four HPV types is expected to substantially reduce the burden of HPV-related disease.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".