A TNM-Based Clinical Staging System of Ocular Adnexal Lymphomas
Bibliographic record
Abstract
Abstract Context. —The ocular adnexal lymphomas (OAL) arise in the conjunctiva, orbit, lacrimal gland, and eyelids. To date, they have been clinically staged using the Ann Arbor staging system, first designed for Hodgkin and later for nodal, non–Hodgkin lymphoma. The Ann Arbor system has several shortcomings, particularly when staging extranodal non– Hodgkin lymphomas, such as OAL, which show different dissemination patterns from nodal lymphomas. Objective. —To describe the first TNM-based clinical staging system for OAL. Design. —Retrospective literature review. Results. —We have developed, to our knowledge, the first American Joint Committee on Cancer–International Union Against Cancer TNM-based staging system for OAL to overcome the limitations of the Ann Arbor system. Our staging system defines disease extent more precisely within the various anatomic compartments of the ocular adnexa and allows for analysis of site-specific factors not addressed previously. It aims to facilitate future studies by identifying clinical and histomorphologic features of prognostic significance. This system is for primary OAL only and is not intended for intraocular lymphomas. Conclusions. —Our TNM-based staging system for OAL is a user-friendly, anatomic documentation of disease extent, which creates a common language for multicenter and international collaboration. Data points will be collected with the aim of identifying biomarkers to be incorporated into the staging system.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".