Defining benchmarks for tolerable risk thresholds in cancer screening: Impact of <scp>HPV</scp> vaccination on the future of cervical cancer screening
Bibliographic record
Abstract
The performance of cervical cancer screening will decline as a function of lower disease prevalence-a consequence of successful human papillomavirus (HPV) vaccination. Replacement of cytology with molecular HPV testing as the primary screening test and adoption of risk-based screening, with less intense screening of vaccinated individuals and initiated at older ages is expected to improve efficiency. However, policy officials may decide to further reduce or eliminate screening as the ratio of benefits to harms continues to decline. To evaluate the level of risk currently tolerated for different cancers in the United States (ie, for which clinical guidelines do not recommend secondary prevention though effective screening methods exist), we used US cancer registry data to compare incidence (2008-2012) and survival (1988-2011) associated with different cancers for which organized screening is recommended and not recommended. The most common cancer at ages 70 to 74 years (ie, age group with highest cancer incidence and reasonable life expectancy to consider screening in the US) satisfying Wilson and Jungner's classic screening criteria was vulvar cancer (incidence = 9/100 000 females). In comparison, the incidence of cervical cancer among females 65 years of age (the upper recommended age limit for screening) was 13 cases per 100 000 females (low as a reflection of effective screening), whereas 10-year survival was 66% (similar to vulvar cancer at 67%). Our approach of defining tolerable risk in cancer screening could help guide future decisions to modify cervical screening programs.
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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 | 0.054 | 0.136 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".