Optimizing technology for cervical cancer screening in high-resource settings
Bibliographic record
Abstract
Although historically successful in reducing the burden of cervical cancer, Papanicolaou (Pap) testing faces numerous limitations. A growing body of evidence suggests that modern screening practice will benefit from primary screening for high-risk human papillomavirus (HPV) infection, the causative agent of cervical cancer. Molecular tests detecting the presence of HPV nucleic acids consistently demonstrate high sensitivity relative to Pap testing, and provide reliable, dichotomous results. Pap cytology is ideally suited to triage HPV-positive cases owing to its high test specificity, and the accuracy of cytological readings will be maximized in high-prevalence conditions. This algorithm of primary HPV testing with Pap triage has been shown to maintain the high sensitivity of HPV testing without compromising Pap cytology's strong ability to rule out falsely positive diagnoses. Given the anticipated decline of high-risk HPV-16 and -18 infections in the emergent post-HPV vaccination era, highly sensitive primary HPV testing is especially warranted. Novel screening technologies that identify HPV viral gene expression continue to emerge and seek to complement current HPV testing by identifying those women who may be at risk of progressive disease. How to best incorporate these new technologies into clinical practice presents our next great challenge. Implementation of novel algorithms for cervical screening is not a trivial task. Avoidance of exceedingly complex screening algorithms is an important priority.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".