TITLE: Cervical Cancer Screening: A Review of a Cost Feasibility Study and Jurisdictional Survey
Bibliographic record
Abstract
In 2010, about 1300 Canadian women will be diagnosed with cervical cancer and approximately 370 women will die from the disease. 1 Cervical cancer incidence and mortality has declined due to screening for cervical cancer using the Papanicalaou (Pap) test. Pap testing results in earlier detection and treatment of cervical cancer. 1 The Pap test (conventional cytology; CC) remains a commonly used technique for cervical cancer screening. In recent years, new techniques have become available. Liquid-based cytology (LBC) enhances the detection of precancerous lesions by improving sample preparation. Testing is also available to detect the 13 high-risk oncogenic human papillomavirus (HPV) DNA types that have been proven to be associated with high-grade lesions of the cervix and invasive cervical cancer. 2 Testing for high-risk HPV types has been proposed both as a primary screening modality and as a method to triage Pap smear results that are equivocal or show low-grade abnormalities. Primary HPV testing is HPV testing used as the principal cervical cancer screening technique and is the only method used, whereas reflex HPV testing involves using a specimen left over from the original LBC sample or a separate sample collected at the same time. 3
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 | 0.027 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".