AACC Guidance Document on Cervical Cancer Detection: Screening, Surveillance, and Diagnosis
Bibliographic record
Abstract
BACKGROUND: Persistent genital infection with high-risk human papilloma virus (hrHPV) causes the vast majority of cases of cervical cancer. Early screening, ongoing surveillance, and accurate diagnosis are crucial for the elimination of cervical cancer. New screening guidelines for testing in asymptomatic healthy populations and management guidelines for managing abnormal results have been published by professional organizations. CONTENT: This guidance document addresses key questions related to cervical cancer screening and management including currently available cervical cancer screening tests and the testing strategies for cervical cancer screening. This guidance document introduces the most recently updated screening guidelines regarding age to start screening, age to stop screening, and frequencies of routine screening as well as risk-based management guidelines for screening and surveillance. This guidance document also summarizes the methodologies for the diagnosis of cervical cancer. Additionally, we propose a report template for human papilloma virus (HPV) and cervical cancer detection to facilitate interpretation of results and clinical decision-making. SUMMARY: Currently available cervical cancer screening tests include hrHPV testing and cervical cytology screening. The screening strategies can be primary HPV screening, co-testing with HPV testing and cervical cytology, and cervical cytology alone. The new American Society for Colposcopy and Cervical Pathology guidelines recommend variable frequencies of screening and surveillance based on risk. To implement these guidelines, an ideal laboratory report should include the indication for the test (screening, surveillance, or diagnostic workup of symptomatic patients); type of test (primary HPV screening, co-testing, or cytology alone); clinical history of the patient; and prior as well as current testing results.
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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.015 | 0.010 |
| Insufficient payload (model declined to judge) | 0.046 | 0.035 |
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".