Conjunctival Squamous Neoplasia: Staging and Initial Treatment
Bibliographic record
Abstract
PURPOSE: To evaluate the clinical relevance of the American Joint Committee on Cancer (AJCC) classification in the initial management of squamous neoplasia of the conjunctiva. METHODS: This retrospective study enrolled 95 histopathologically proven cases of treatment-naive conjunctival squamous neoplasia. Tumors were classified into 4 histological groups: conjunctival intraepithelial neoplasia (CIN) with mild dysplasia (grade 1/3), moderate dysplasia (grade 2/3), severe dysplasia (grade 3/3 or carcinoma in situ), and invasive squamous cell carcinoma (SCC). Clinical findings such as tumor location, largest basal diameter, growth pattern, and adjacent structures involved were recorded. RESULTS: CIN was observed in 74 cases (78%), and SCC was noted in 21 cases (22%). Based on the AJCC classification, all the 74 cases of CIN were classified as Tis (tumor in situ). Among the invasive SCC, there were 3 T1 tumors, 2 T2 tumors, and 16 T3 tumors. Complete excision with or without adjuvant therapy was selected as initial treatment in 80% of cases (76/95). Two cases of SCC with scleral invasion were treated using brachytherapy. CONCLUSIONS: The AJCC stage does not correlate with the initial treatment of CIN. The AJCC T3 category should be reviewed to differentiate diffuse SCCs with broad surface extension from tumors with deep scleral invasion.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 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".