Surrogate indicators of sensitivity in gynecologic cytology: Can they be used to improve the measurement of sensitivity in the laboratory?
Bibliographic record
Abstract
BACKGROUND: Measuring the sensitivity of screening in gynecologic cytology in real life is problematic. However, other quality measures may correlate with sensitivity, including the atypical squamous cells (ASC)/squamous intraepithelial lesion (SIL) ratio. Whether these other measures can function as "surrogate indicators" for sensitivity and improve the assessment of sensitivity in the laboratory is not known. MATERIALS AND METHODS: We compared multiple quality measures with true screening sensitivity in a variety of situations. RESULTS: The abnormal rate, ASC rate, and ASC/SIL ratio were all highly correlated (r =.83 or greater) with sensitivity when the overall laboratory sensitivity was low (85%) but became less correlated (.64 or less) or uncorrelated when the screening sensitivity was higher (88% or 95%, respectively). Sensitivity was more highly correlated with the abnormal rate than the ASC/SIL ratio at low screening sensitivity. While thresholds could be set that were highly sensitive and specific for suboptimal screening, these thresholds were often less than one standard deviation away from the mean. CONCLUSION: The correlation of the abnormal rate and the ASC/SIL ratio with sensitivity depends on overall sensitivity. Standards to define minimum screening sensitivity can be defined, but these standards are relatively narrow. These features may limit the utility of these quality measures as surrogates for sensitivity.
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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 | 0.140 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| 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".