Challenges of False Positive and Negative Results in Cervical Cancer Screening
Bibliographic record
Abstract
Abstract Background: Liquid-based cytology (LBC) molecular testing for human papillomavirus (HPV) infection andcombinations are practical modalities for cervical-screening. While life-saving, false positive and negative results are possible, leading to potential over or under treatment. Quantifying this is complicated by the increasing number of options available, including parallel co-testing and sequential triage. As HPV vaccination rates increase, it also has a potential impact onscreening test performance and interpretation of results. Methods:A modelling approach was used to compare different screening modalities in terms of Cervical intra-epithelial neoplasia (CIN) grade 2 and 3 detected and missed, false positives leading to excess colposcopy, and number of tests required to achieve a given accuracy. The positive predictive value (PPV) and negative predictive value (NPV) of different modalities were simulated under varying levels of HPV vaccination. Results:The model suggested that in a cohort of 1000 women, LBC screening typically misses 4.9 cases (95% Confidence Interval (CI) 3.5-6.7), with 95 (95% CI: 93-97%) excess colposcopies. With primary HPV testing, 2.0 (95% CI:1.9-2.1) were missed with 99 (95% CI:98-101) excess colposcopies. Co-testing reduced missed cases to 0.5 (95% CI:0.3-0.7) but dramatically increased excess colposcopy referral (184, 95% CI:182-188). Conversely, triage testing with reflex screening substantially reduced excess colposcopy to 9.6 (95% CI:9.3-10) at the cost of missing more cases (6.4, 95% CI:5.1-8.0). Over a life-time of screening, women who always attend co-testing hada 93.8-100% chance of a false positive over screening life-time. For annual, 3-year, and 5-year triage testing (either LBC with HPV reflex or vice-versa), lifetime risk of a false positive is 35.1%, 13.4%, and 8.3% respectively.Results of this work indicate that as HPV vaccination rates increase, HPV based screening approaches result in fewer unnecessary colposcopies than LBC approaches. Conclusion:Clinical relevance of cervical cancer screening is crucially dependent upon prevalence of cervical dysplasia and/or HPV infection or vaccination in a population, and the sensitivity and specificity of modalities employed. Although screening is life-saving, false negatives and positives inevitably occur, and over-testing runs risk of significant harm, including potential over-treatment. As HPV becomes less common, HPV-based modalities may have greater utility.
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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 | 0.047 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".