Bibliographic record
Abstract
OBJECTIVE: To apply the updated epithelial salivary gland classification scheme to a large cohort of lacrimal gland tumors so as to provide an updated lacrimal gland tumor classification scheme. METHODS: A retrospective multicenter cohort study of 118 cases of epithelial neoplasia was undertaken. Main outcome measures included pathologic analysis, subtyping, and survival. RESULTS: Of 118 cases, 17 (14%) were reclassified using the proposed expanded classification scheme based on the current World Health Organization classification of salivary gland tumors. The most frequent neoplasms were pleomorphic adenoma and adenoid cystic carcinoma, of which we highlight more unusual histologic features. Three tumors were found to be unclassifiable with the updated scheme, with 2 having histologically malignant features. Deficiencies and variations in pathologic assessment were noted. Variation in the histologic findings of pleomorphic adenoma and assessment of the extent of invasion of carcinoma ex pleomorphic adenoma were highlighted. CONCLUSIONS: The use of the more histologically diverse classification of salivary gland tumors can be successfully applied to the epithelial lacrimal gland neoplasms. This expanded classification system led to reclassifying 14% of cases. Currently, there are no consistent pathologic standards for processing and evaluating these lesions.
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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".