Bibliographic record
Abstract
Abstract Background. —Granular cell changes can be observed in a variety of benign and malignant tumors, and are seen more commonly in granular cell tumors, which in about 5% of cases develop in the breast. Granular cells also have been observed in sites of previous trauma, such as surgery, and are found to be inflammatory reactions of histiocytic origin. Methods and Results. —We investigated, morphologically and immunohistochemically, 2 granular cell lesions occurring in mastectomy scars after surgery for carcinoma. Both lesions were composed of strands and nests of large granular cells, haphazardly set in a background of fibrous tissue, with sparse inflammatory infiltrates. Several tortuous hypertrophic nerve bundles were also embedded in the fibrous tissue. A few of these nerve bundles showed degenerative changes and contained granular cells. Immunohistochemically, granular cells were positive for S100 protein, neuron-specific enolase, vimentin, and CD68 antigen. Conclusions. —We consider these proliferative lesions of peripheral nerves to have the features of both granular cell tumor and traumatic neuroma. These cases indicate that traumatic neuroma can undergo extensive granular cell changes and constitute a previously unrecognized entity, which we provisionally label granular cell traumatic neuroma. Granular cell traumatic neuroma has to be taken into consideration when evaluating lesions occurring at mastectomy scars and should be differentiated from malignant tumors with granular cells, such as apocrine carcinoma and alveolar soft part sarcoma.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".