Utility of Ber-EP4 and MOC-31 in Basaloid Skin Tumor Detection
Bibliographic record
Abstract
Ber-EP4 has been the traditional immunostain used for the detection of basaloid skin tumors. Recently, MOC-31 has shown be superior to Ber-EP4 in the detection of basosquamous basal cell carcinoma (BCC) and many centers are now using both Ber-EP4 and MOC-31 antibodies together to detect these lesions. The objective of this study was to compare the utility of using both Ber-EP4 and MOC-31 immunostains in the detection of basaloid skin tumors and to better characterize the previously unknown staining properties of MOC-31 in cutaneous lesions. To do this, 76 basaloid skin tumors stained with both Ber-EP4 and MOC-31 were obtained. Diagnoses included basosquamous BCC, Merkel cell carcinoma, adenoid cystic carcinoma, microcystic adnexal carcinoma, sebaceous carcinoma, trichoepithelioma, trichoblastoma, sebaceous adenoma, sebaceoma, and follicular induction overlying dermatofibroma. The distribution and intensity of Ber-EP4 and MOC-31 staining in these lesions was scored. These scores were analyzed using a truth table, χ test, and Pearson correlation tests. The overall mean and SD of the scores were also obtained. Overall, we found Ber-EP4 and MOC-31 to be statistically equivalent immunostains for the diagnosis of basaloid skin tumors. We recommend the use of only one of these antibodies and favor MOC-31 for the detection of basaloid skin tumors. We also describe MOC-31 staining properties in different cutaneous lesions.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".