<scp>P</scp>ap smears with glandular cell abnormalities: Are they detected by rapid prescreening?
Bibliographic record
Abstract
BACKGROUND: Rapid prescreening (RPS) is one of the quality assurance (QA) methods used in gynecologic cytology. The efficacy of RPS has been previously studied but mostly with respect to squamous lesions; in fact, there has been no study so far specifically looking at the sensitivity of RPS for detecting glandular cell abnormalities. METHODS: A total of 80,565 Papanicolaou (Pap) smears underwent RPS during a 25-month period. A sample was designated as "review for abnormality" (R) if any abnormal cells (at the threshold of atypical squamous cells of undetermined significance/atypical glandular cells [AGC]) were thought to be present or was designated as negative (N) if none were detected. Each sample then underwent full screening (FS) and was designated as either R or N and also given a cytologic interpretation. RESULTS: The final cytologic interpretation was a glandular cell abnormality (≥AGC) in 107 samples (0.13%); 39 of these (36.4%) were flagged as R on RPS. Twenty-four patients (33.8%) out of 71 who had histologic follow-up were found to harbor a high-grade squamous intraepithelial lesion or carcinoma; 13 of those 24 Pap smears (54.2%) had been flagged as R on RPS. Notably, 11 AGC cases were picked up by RPS only and not by FS and represented false-negative cases; 2 of these showed endometrial adenocarcinoma on histologic follow-up. CONCLUSIONS: Pap smears with glandular cell abnormalities are often flagged as abnormal by RPS, and this results in a sensitivity of 36.4% (at the AGC threshold). Most importantly, some cases of AGC are detected on Pap smears by RPS only, and this demonstrates that RPS is a valuable QA method.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".