Outcomes in Orbit‐Sparing Versus Orbit‐Sacrificing Surgery for Sinonasal Malignancies With Orbital Involvement: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background: Sinonasal malignancies with orbital involvement may be managed with orbit-sacrificing or orbit-preserving surgical approaches, with a recent shift towards orbital preservation to reduce postoperative morbidity while maintaining oncological success. The current clinical data on the most optimal approach for managing such locally advanced tumours remains inconclusive. Methods: PubMed, Embase, and SCOPUS were searched from inception to 12 June 2024 for longitudinal studies investigating oncological and functional outcomes in sinonasal malignancies with orbital involvement managed with orbit-sacrificing vs. orbit-preserving surgery. Two independent authors selected relevant articles, extracted data, assessed bias using the Newcastle-Ottawa Scale and evaluated quality of evidence following the Grading of Recommendations, Assessment, Development and Evaluations framework. Random-effects meta-analysis was performed to synthesise pooled oncological outcomes, while descriptive reviews were performed for functional outcomes. Results: This systematic review and meta-analysis of 12 studies and 758 participants found that the 5-year overall survival rate of patients managed with orbit-preserving surgery (55%, 95% CI: 0.32-0.76) was comparable to that in patients managed with orbit-sacrificing surgery (53%, 95% CI: 0.34-0.70). The 5-year recurrence-free survival rate was significantly higher in patients managed with orbit-preserving surgical intervention (64%, 95% CI: 0.44-0.80), compared to those with orbit-sacrificing surgery (48%, 95% CI: 0.13-0.84). Descriptive review showed good functional outcomes in patients managed with orbit-preserving surgery. Conclusion: Orbit-preserving surgery in selected cases of sinonasal malignancies with orbital involvement is oncologically safe and can allow for the maintenance of a functionally useful eye. Greater number of large-scale, robust studies are required to further evaluate the outcomes in tumours with different characteristics.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.048 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".