Bibliographic record
Abstract
Toker cells are inconspicuous cytokeratin 7 positive cells that should be distinguished from intraepidermal involvement by malignant cells seen in Paget's disease (PD) of the nipple. The aim of our study was to quantitatively assess the number and pattern of distribution of Toker cells in nipples without PD in mastectomy specimens. Sequential sections from the nipple of 173 mastectomies were evaluated using HE and immunohistochemistry. Two breast pathologists reviewed the stains and recorded the number and pattern of distribution of CK7 positive cells (CK7+) and their HER2/neu, ER and PR status. There were 152/173 (88.4%) cases with CK7+ cells. The mean number of CK7+ cells per section was 22 (range 0-200) and the mean number of CK7+ cells per field at 10x was 10 (range 0-106). 10/37 (27%) of nipples from prophylactic mastectomies and 15/136 (11.1%) of mastectomies for cancer displayed over 10 CK7+ cells in an area of 10x. In 32/173 (18.5%) CK7+ cells formed small groups or clusters. Notably these cells were not restricted to the basal part of the epidermis. None of the groups/clusters of Toker cells were appreciated on HE stain and all were HER2/neu, ER and PR negative. The absence of any CK7+ cells in 20 cases occurred in hyperpigmented epidermis, possibly obscuring rare positive cells. Toker cells are frequently present in the epidermis of the nipple and are more prevalent than appreciated on routine HE stain. These cells can be present in clusters and can have pagetoid pattern of distribution. They occur in nipples of cancer patients with or without PD and in prophylactic mastectomies performed for high risk patients. The substantial number and the clustering of Toker cells should not be mistaken for PD of the nipple. Unlike PD, Toker cells have small bland nuclei and are characterized by CK7+ and HER2/neu ER and PR negative immunoprofile.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".