Metaanalysis of the accuracy of rapid prescreening relative to full screening of pap smears
Bibliographic record
Abstract
BACKGROUND: Efficient quality assurance and improvement measures are essential ingredients in a well organized cytology-based program for cervical carcinoma screening. Various pap smear review procedures, aiming for optimization of accuracy, are described throughout the literature. Evaluation and synthesis of those methods are needed. In a previous study, we pooled data on the diagnostic quality of rapid reviewing (RR) of cervical smears initially reported as normal or unsatisfactory. We now focus on rapid prescreening (RPS) of unreported smears. METHODS: Six published studies on the accuracy of RPS relative to subsequent full screening were pooled using metaanalytic methods. Individual and pooled sensitivity, specificity, and predictive values were assessed using forest plots. Random effect pooling methods were used for interstudy heterogeneity. Variation in sensitivity according to influencing factors was explored by metaregression. RESULTS: The pooled average sensitivity of RPS was 64.9% (95% confidence interval [CI] 50.7-79.1%) for all abnormalities, 72.6% (95% CI 60.6-85.2%) for low-grade lesions or more severe, and 85.7% (95% CI 77.8-93.6%) for high-grade lesions or more severe. The pooled specificity was estimated at 96.8% (CI 95.8-97.8%). The sensitivity increased significantly with duration of screening and decreased with workload. Almost 3% of all abnormal slides were detected only by RPS (2.8%; CI 0.0-5.8%). This is comparable to the proportion of false-negative smears detectable by RR. CONCLUSIONS: Rapid prescreening has a high yield for severe dysplasia and shows diagnostic properties that support its use as a quality control procedure in cytologic laboratories. We showed previously that RR is superior to full reviewing of a 10% random sample of negative slides (10% FR). Because the yield of additional abnormalities found by RR and RPS is comparable, we expect RPS to be more efficient than 10% FR as well.
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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 | 0.034 | 0.069 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.065 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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 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".