Comparison of American Joint Committee on Cancer TNM-based Staging System (7th edition) and Ann Arbor Classification for Predicting Outcome in Ocular Adnexal Lymphoma
Bibliographic record
Abstract
OBJECTIVE: To compare the TNM and Ann Arbor staging systems in predicting outcome in ocular adnexal lymphoma (OAL). METHODS: Retrospective review of the clinical, imaging and histopathologic records of OALs between 1986 and 2009. Outcome measures included local recurrence and progression. RESULTS: One hundred and sixty patients of OAL were included. Mean age was 65 ± 15 years (range 20-97) and 68 (43%) were male. The median follow-up of all OAL patients was 65 months (range 2.5-238). Histopathology identified low-grade, indolent B-cell lymphomas in 140 patients (87.5%) and rest had aggressive grades. Of 134 indolent OAL patients, those with unilateral disease had a 10-year progression free survival of 72% versus 48% in their bilateral counterparts (p = 0.001). Amongst unilateral OAL patients staged within the T1-2 group, a significantly better outcome was noted for patients without nodal or metastatic involvement compared to those with such involvement (p = 0.0001). The above observations helped to formulate a simple scoring system to prognosticate OALs based on their laterality and node/metastatic status. Amongst the 3 groups identified, group 1 with a score of 0 (unilateral OALs with no nodes or metastasis) had a 10-year progression free survival of 75%; group 2 with score 1 (either bilateral or positive nodes/metastasis) 50% and group 3 with score 2 (both bilateral OAL with positive nodes/metastasis) zero at 10 years (p < 0.00001). CONCLUSIONS: The TNM-based staging system better predicts outcome in OAL than the Ann Arbor system primarily by delineation of bilateral disease and nodal/metastatic involvement at presentation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".