Bibliographic record
Abstract
BACKGROUND: Efficient quality control (QC) is essential to ensure high sensitivity of Papanicolaou (Pap) smears. For this purpose, rescreening of 10% random negative smears is ineffective. Rapid rescreening (RR) of all negative Pap smears is more practical and has received widespread acceptance, especially in Europe, although its sensitivity is difficult to monitor and its retrospective nature may influence the vigilance of the screeners. The method of rapid prescreening (RPS) overcomes these drawbacks because rapid review of Pap smears occurs before routine full screening. METHODS: All routine conventional Pap smears over 2 months underwent RPS by 12 cytotechnologists. Approximately 30 seconds were allowed to prescreen each slide. The presence of abnormal cells (atypical squamous cells of undetermined significance [ASCUS] or above), infection or endometrial cells detected on RPS was documented. All slides subsequently underwent routine full screening. Results of both screening methods were compared. RESULTS: Of a total of 8364 Pap smears, 310 (3.7%) cases were categorized as abnormal after final diagnosis. Of those, 135 were also detected on RPS (sensitivity of 43.5%). Seventeen abnormal cases were detected only on RPS: these consisted of 13 ASCUS cases, 3 low-grade squamous intraepithelial lesions, and 1 high-grade squamous intraepithelial lesion. The sensitivity of RPS for infections and endometrial cells was 51.6% and 28.3%, respectively. Implementation of RPS did not significantly impact the work flow in our laboratory. CONCLUSIONS: RPS is an efficient and practical QC tool. It is a reliable method with which to monitor sensitivity and reduce the false-negative rate, and because it is done before finalizing the case, it allows for timely corrections to the diagnosis and avoids the need to amend reports.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".