Global incidence and prevalence of malignant orbital tumors
Bibliographic record
Abstract
Purpose: Aims to provide an overview of the contemporary epidemiology of malignant orbital tumors by analyzing population-based incidence patterns across various regions worldwide. Methods: In this article, we retrieved orbital malignancy data from the MEDLINE database and analyzed the incidence and prevalence of orbital malignancies worldwide. We performed the literature search by searching on the Mesh terms for malignant orbital tumors ("orbital", "tumor", "lymphoma", "malignant", "cancer", "incidence", and "epidemiology"). All included studies were published between 1993 and 2023 and were written in English. Results: Ocular or ophthalmic lymphoma most frequently occurred in the orbit, with a prevalence ranging from 47% to 54%. The incidence of malignant orbital tumors was increasing in the USA (2.0 per million (1981-1993), Netherlands (0.86 (1981-1985) to 2.49 (2001-2005) per million) and South Korea (0.3-0.8 per million (1999-2016)), respectively. Ophthalmic lymphoma which includes orbit lymphoma was increasing in Canada (0.17-1.47 per million (1992-2010)), Denmark (0.86 per million (1981-1985) to 2.49 per million (2001-2005)), respectively. Conclusions: The predominant primary malignant orbital tumor in adults was lymphoma. Ocular or ophthalmic lymphoma most frequently occured in the orbit. The limited data available suggested an increasing trend in the incidence of malignant orbital tumors in each country included, which were mainly attributed to the increase in lymphoma. Generally, incidence rates were found to increase with advancing age, with no difference between males and females.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".