The Value of a Monoclonal Anti-Epithelial Antibody (mAB lu-5) in the Differential Diagnosis of Tumors
Bibliographic record
Abstract
The usefulness of a monoclonal anti-epithelial antibody, mAB lu-5, was assessed in the histologic differential diagnosis of 102 formalin-fixed, paraffin-embedded tumors including various carcinomas, sarcomas and lymphomas. A variety of nontumorous tissues were also evaluated. Although mAB lu-5 failed to provide conclusive results in a few cases, in general, it was found to be a reliable immunohistochemical marker of tumorous and nontumorous epithelial cells. Immunostaining with mAB lu-5 did not distinguish between tumorous and nontumorous tissues and between benign and malignant tumors. Further work is required to clarify the significance of strong immunoreactivity noted in chorionic epithelium and pituitary corticotrophs.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".