Is the Expression Pattern of BD ProExC the Same as Ki-67? A Comparative Analysis in Cervical Biopsies
Bibliographic record
Abstract
BD ProExC (ProExC) and Ki-67, both used as biomarkers for high-grade cervical intraepithelial neoplasia (HG CIN), yield discordant staining in some cervical biopsies. This study compared ProExC and Ki-67 expression in 197 cervical biopsies with consensus diagnoses (55 negative, 21 atypical squamous metaplasia, 61 low-grade CIN, and 60 HG CIN). Percentages of immunostained nuclei were evaluated by 2 pathologists yielding 68 (35%) cases with discordant ProExC/Ki-67 immunostaining for analysis. In 78% of cases the difference in staining involved <25% of lesional cells. This was noted across all morphologic diagnoses, being most frequent in HG CIN. Discordant staining involving >50% of lesional cells occurred in 22% of discrepant cases, being most frequent in atypical squamous metaplasia. Using staining in >50% of lesional nuclei as a positive result, positive/negative discordance occurred in 25 cases (13% of all cases) including 18% of HG CIN cases. Fourteen cases were ProExC+ (7 of which were strongly p16+) and 11 were Ki-67+ (6 of which were strongly p16+).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".