Cervical cytology reproducibility and associated clinical and demographic factors
Bibliographic record
Abstract
BACKGROUND: Although the Pap test has been the standard screening method for cervical precancer/cancer detection, it has been criticized for having a relatively low sensitivity and a low reproducibility between pathologists. There is limited knowledge about inter-rater agreement and what clinical and demographic factors are associated with disagreements between pathologists reading the same Pap smear. METHODS: This study aimed to assess inter- and intra- rater agreement of the Pap smear in 1619 cytologic slides with biopsy confirmation, using kappa statistics. Clinical and demographic factors associated with higher odds of inter-rater agreement were also examined and stratified by histologic diagnosis grade. RESULTS: Using a five grade classification system, the overall kappa statistics for total, inter-rater, and intra-rater samples were 0.62, 0.57, and 0.88 (unweighted) and 0.83, 0.81, and 0.95 (weighted), respectively. In stratified analyses by histologic grade, total kappas ranged from 0.40 (atypia) to 0.64 (human papilloma virus/CIN 1). Factors such as referral for abnormal Pap test (diagnostic vs screening population), recruiting site, and parity were found to be associated with higher agreement between the two cytologic readings. CONCLUSIONS: We observed relatively higher levels of agreement compared with other studies. However, variability was considerable and agreement was generally moderate, suggesting that cervical screening test accuracy and reproducibility needs to be improved.
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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 | 0.030 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".