The Accuracy of the Papanicolaou Smear in the Screening and Diagnostic Settings
Bibliographic record
Abstract
OBJECTIVE: We evaluated the performance of the Papanicolaou smear in screening and diagnostic settings. STUDY DESIGN: We analyzed Papanicolaou smear results of 1,850 women recruited into a clinical trial to evaluate an emerging technology for the detection of cervical cancer. Screening and diagnosis groups were based on the history of previous Papanicolaou smear results. We calculated sensitivities, specificities, positive and negative likelihood ratios (LR+ and LR-), receiver operating characteristic curves, and areas under the receiver operating characteristic curve (AUC). RESULTS: In the screening group, by defining disease as cervical intraepithelial neoplasia (CIN) 2,3/cancer or worse and using high-grade squamous intraepithelial lesion (HSIL) as the test cutpoint, the AUC was 0.689, and the LR+ and LR- were 39.25 and 0.67, respectively. In the diagnosis group, the AUC was 0.764, and the LR+ and LR- were 3.79 and 0.56, respectively. By defining disease as human papillomavirus/CIN 1 or worse and HSIL as the test cutpoint, the AUC was 0.586, and the LR+ and LR- were 17.01 and 0.92 in the screening group; in the diagnosis group, the AUC was 0.686, and the LR+ and LR- were 2.77 and 0.75, respectively. CONCLUSIONS: In a screening setting, a Papanicolaou smear result of HSIL or worse is 39 times more likely in a patient with CIN 2,3/cancer than in a patient without it. This compares to 4 times more likely in the diagnostic setting. The magnitude of the positive likelihood ratio observed in the screening group indicated that abnormal Papanicolaou smear results obtained in the screening setting should have more impact on clinical decision making than those from results obtained in the diagnostic setting.
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 | 0.018 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".