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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".