Tumors of the orbit and periorbital region: 20-year experience of the N.N. Blokhin National Medical Research Center of Oncology
Bibliographic record
Abstract
Introduction. Neoplasms of the orbit and periorbital region are usually represented by tumors of the eyelid skin skin of the periorbital region and paranasal mucosa. The most common histological types of this pathology are squamous cell and basal cell carcinomas. The main indications for orbital exenteration are tumor lesions in the extraocular muscles and orbital apex and irreversible vision loss in the affected eye at the time of surgery planning. In the absence of these pathologies organ-sparing surgery is recommended. Resection of orbital and periorbital tumors (including orbital exenteration) leads to cosmetic and functional defects therefore surgical treatment of this area should be performed only if other methods are impossible or ineffective.Aim. To analyze the experience of resection of orbital and periorbital tumors and to evaluate short- and long-term results of surgical treatment of this pathology.Materials and methods. Retrospective analysis of patients with tumors of the orbit and periorbital region who underwent organ-sparing and exenteration surgeries between 2003 and 2023 was performed.Results. Overall 5-year survival was 55 %. It varied depending on a number of factors. The best 5-year survival rates were observed for basal cell carcinoma of the eyelid skin and periorbital region (83 %) the worst for squamous cell carcinoma (34 %) and melanoma (41 %). For localized tumors (Т1) overall survival was 76 % for locally advanced (Т4) – 43 %.Conclusion. Orbital and periorbital neoplasms are characterized by a variety of tumor morphological types and locations which requires multidisciplinary approach to treatment and rehabilitation of patients with this pathology.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".