Histochemical analysis and immunohistochemical profile of mucoepidermoid carcinoma of the conjunctiva
Bibliographic record
Abstract
PURPOSE: To elucidate the distinct histochemical and immunohistochemical profile of mucoepidermoid carcinoma of the conjunctiva (MECC) and to determine which combination of stains is most useful in diagnosing MECC and differentiating it from squamous cell carcinoma of the conjunctiva (SCC) in cases where the clinical or cytological findings are not definitive. METHODS: Eight specimen of MECC from 4 patients and 4 specimens of SCC from 4 patients were examined using a variety of special stains and immunohistochemical markers. The results were then analyzed for usefulness in diagnosing MECC. RESULTS: The most useful markers in diagnosing MECC and differentiating it from SCC are mucicarmine, colloidal iron, and alcian blue all with sensitivities of 88%, and a specificity of 100%; CEA with a sensitivity of 83% and a specificity of 75%; and, mucin-1 with a sensitivity of 100% and a specificity of 25%, but which showed a distinct pattern of staining of MECC when compared to SCC. In our series, the sensitivity of the CK7+/CK20- combination for MECC was only 38%. CONCLUSIONS: The most useful stains in ruling out SCC in a suspected case of MECC were shown to be mucicarmine and the glycosaminoglycan (GAG) stains. However, in cases where mucicarmine and the GAG stains are negative or difficult to interpret and there is suspicion of a diagnosis of MECC, CEA and mucin-1 may be helpful for this diagnosis. The findings of CK7+/CK20- also may be of assistance, but are not as sensitive when compared to analogous salivary gland lesions, when differentiating MECC from SCC.
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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 | 0.000 |
| 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".