Current State of Cytologic-Histologic Correlation Implementation for North American and International Laboratories: Results of the College of American Pathologists Cytopathology Committee Laboratory Practices in Gynecologic Cytology Survey
Bibliographic record
Abstract
CONTEXT.—: The College of American Pathologists (CAP) updated the Laboratory Accreditation Program Cytopathology Checklist to assist laboratories in meeting and exceeding the Clinical Laboratory Improvement Amendments standards for gynecologic cytologic-histologic correlation (CHC). OBJECTIVE.—: To survey the current CHC practices. DESIGN.—: Data were analyzed from a survey developed by the committee and distributed to participants in the CAP Gynecologic Cytopathology PAP Education Program mailing. RESULTS.—: Worldwide, CHC practice is nearly universally adopted, with an overall rate of 87.0% (568 of 653). CHC material was highly accessible. CHC was commonly performed real time/concurrently at the time the corresponding surgical pathology was reviewed. Investigation of CHC discordances varied with North American laboratories usually having a single pathologist review all discrepant histology and cytology slides to determine the reason for discordance, while international laboratories have a second pathologist review histology slides to determine the reason for discordance. The cause of CHC discordance was primarily sampling issues. The more common statistical metrics for CHC monitoring were the total percentage of cases that correlated with subsequent biopsies, screening error rate by cytotechnologist, and interpretative error rate by cytotechnologist. CONCLUSIONS.—: Many laboratories have adopted and implemented the CHC guidelines with identifiable differences in practices between North American and international laboratories. We identify the commonalities and differences between North American and international institutional practices including where CHC is performed, how CHC cases are identified and their accessibility, when CHC is performed, who investigates discordances, what discordances are identified, and how the findings affect quality improvement.
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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 | 0.029 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".