The Epidemiology of Cervical Cancer
Bibliographic record
Abstract
Cervical cancer is one of the most common neoplastic diseases affecting women, with a combined worldwide incidence of almost half a million new cases annually, second only to breast cancer. Basic and epidemiologic research conducted during the past 15-20 years have provided overwhelming evidence for an etiologic role for infection with certain types of sexually-transmitted human papillomavirus (HPV) as the primary cause of cervical cancer. The relative risks of cervical cancer following HPV infection as ascertained in case-control and cohort studies are among the highest in cancer epidemiology. The available evidence indicates that the HPV-cervical cancer association satisfies all relevant causal criteria for public health action. Other cervical cancer risk factors, such as smoking, parity, use of oral contraceptives, diet, other infections, and host susceptibility traits must be understood in the context of mediation of acquisition of HPV infection or in influencing events of the natural history of cervical neoplasia that occur following the establishment of a persistent HPV infection. Virtually all cervical carcinoma specimens contain HPV DNA, which suggests that HPV infection is a necessary cause of cervical neoplasia. This is the first instance in which a necessary cause has been demonstrated in cancer epidemiology--a realization that has obvious implications for primary and secondary prevention of this neoplastic 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".