Ultrasound detection of iatrogenic injury during peribulbar eye block: a cadaveric study
Bibliographic record
Abstract
BACKGROUND: Ophthalmic eye blocks, such as retrobulbar, peribulbar and sub-Tenon's, are traditionally conducted "blind". Complications are rare but potentially devastating. Life-threatening complications include brain stem anesthesia and local anesthetic toxicity, whereas sight-threatening complications include globe perforation, optic nerve damage and ocular muscle damage. Ultrasound permits a view of orbital structures and can be used to guide needle placement. The ultrasound appearances of unintended local anesthetic injection into vital orbital structures have not been documented. This study aimed to record the ultrasound appearances of unintended injection locations. METHODS: The spherical shape of the eyeballs of three soft-fix Thiel embalmed human cadavers were restored using glycerol. Iatrogenic injury in peribulbar block was then simulated through injection of printers' ink mixed with Thiel embalming fluid. Ultrasound was used to guide the needles and the tips were redirected to lie within the globe, lateral rectus and optic nerve. Ultrasound images were recorded during injection. The orbital cavities were then dissected via a superior approach to record the location and extent of injectate spread. RESULTS: Real-time globe rupture, ocular muscle injection and optic nerve injection were visible using ultrasound. Characteristic appearances were identified in each case. Dissection confirmed needle and injection placement. CONCLUSIONS: The ultrasound appearance of block complications is important to document and should be an integral part of regional anesthesia training. This study is the first to provide such images for ophthalmic nerve blocks. It offers ophthalmic anesthetists and ophthalmologists the potential to diagnose severe complications rapidly and accurately with a potential impact on patient safety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".