Measuring the Significance of Field Validation in the College of American Pathologists Interlaboratory Comparison Program in Cervicovaginal Cytology: How Good Are the Experts?
Bibliographic record
Abstract
Abstract Context.—Expert opinion is often used as a gold standard for gynecologic cytology in the evaluation of new technologies, in the legal setting, and in the validation of cases for use in educational programs and proficiency testing. However, the reliability of expert opinion alone in selecting slides of a specific cytodiagnosis that can be reproducibly and reliably identified by subsequent reviewers has not been determined. Objective.—To assess the ability of expert opinion to select slides that are validated in subsequent reviews. Design.—In the College of American Pathologists Interlaboratory Comparison Program in Cervicovaginal Cytology, each case in every cytodiagnostic category is accepted for circulation only after review by 3 expert cytopathologists. The percentage of these cases that could not be reliably and reproducibly identified by program participants for each cytodiagnostic category (“failed field validation”) was determined during the duration of the program from 1989 to 2004. Results.—More than 10 000 conventional smears and ThinPrep cases were selected by the expert panel for circulation. Of these selected slides, 19% of conventional smears and 15% of ThinPrep specimens failed field validation. Compared with the overall slide performance, significantly higher percentages (P < .001) of conventional smears with reference diagnoses of unsatisfactory (51.7%), repair (58%), or low-grade intraepithelial lesion (31.8%) and of ThinPrep specimens with reference diagnoses of unsatisfactory (54.5%) and repair (76.9%) failed field validation. In contrast, significantly lower percentages of conventional smears with reference diagnoses of squamous cell carcinoma (4.5%), high-grade squamous intraepithelial lesion (9%), Trichomonas vaginalis infection (11.7%), or herpes (9.9%) and of ThinPrep specimens with reference diagnoses of adenocarcinoma (5.1%), herpes (2.1%), and fungal organism consistent with Candida (8.4%) failed field validation (P < .001 for all). Conclusions.—Between 15% and 19% of gynecologic cytologic cases that have been selected by expert cytopathologists as good examples of cytodiagnostic abnormalities fail field validation. The proportion of cases failing field validation varies with cytodiagnostic category, but it occurs in all cytodiagnostic entities.
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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 | 0.153 | 0.258 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".