Correlation of cytotechnologists' parameters with their performance in rapid prescreening of papanicolaou smears
Bibliographic record
Abstract
BACKGROUND: Efficient quality control is essential to ensure high sensitivity of Papanicolaou (Pap) smears. For this purpose, rescreening of 10% random negative smears is increasingly felt to be 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 precedes full screening. METHODS: All routine conventional Pap smears (n = 8364) over 2 months underwent RPS by 12 cytotechnologists, followed by full screening. Data were analyzed to determine correlation between the RPS sensitivity of individual cytotechnologists and both their sensitivity in full screening and their years of experience as cytotechnologists. RESULTS: There was a striking variability in sensitivity (15.4%-72.7%) among the 12 screeners with an atypical squamous cells of undetermined significance (ASCUS) threshold. There was no correlation between RPS sensitivity of individual cytotechnologists with either their sensitivity in full screening or their years of experience as cytotechnologists. CONCLUSIONS: The skills required of a cytotechnologist for achieving a high sensitivity in RPS are apparently different from those of full screening and are independent of the sensitivity of the screeners at full screening or of the years of experience as cytotechnologists.
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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 | 0.007 | 0.064 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".