Ophthalmic Complications After Dental Procedures: Scoping Review
Bibliographic record
Abstract
Introduction: Ocular complications associated with dental procedures are diverse but have been primarily reported through case reports and series, with no comprehensive reviews to date. The underlying mechanisms of these complications are often poorly understood by medical professionals, partly due to limited interdisciplinary education. This review aims to bridge this gap by summarizing the relevant anatomical connections between the oral and ocular regions, exploring the mechanisms through which dental procedures may lead to ophthalmic complications, and detailing their clinical presentations, progression, and potential management and preventive strategies. Methods: Published case reports and case series from 1950 to October 2024 that described ophthalmic complications in human patients following dental procedures were included in this scoping review. Results: Dental procedures can give rise to a variety of ophthalmological complications, whether neuro–ophthalmic (e.g., diplopia, ptosis, or vision loss), vascular (e.g., retrobulbar hemorrhage or cervical artery dissection), infectious (e.g., orbital cellulitis or abscess), mechanical (e.g., orbital trauma or fractures), or air-related (e.g., orbital and subcutaneous emphysema). Conclusions: Most of the ophthalmological complications following dental procedures are often reversible, but some can be vision-threatening or lead to permanent sequelae if not promptly recognized and managed. Prevention through precise technique and anatomical awareness, early identification of symptoms, and timely multidisciplinary collaboration are crucial to minimizing risks and ensuring better patient outcomes.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".