Real-Time Evaluation of Diffusion of the Local Anesthetic Solution During Peribulbar Block Using Ultrasound Imaging and Clinical Correlates of Diffusion
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The aims of this prospective observational study were to assess the incidence of intraconal spread during peribulbar (extraconal) anesthesia by real-time ultrasound imaging of the retro-orbital compartment and to determine whether a complete sensory and motor block (with akinesia) of the eye is directly related to the intraconal spread. METHODS: Ultrasound imaging was performed in 100 patients who underwent a surgical procedure on the posterior segment of the eye. All patients received a peribulbar block using the inferolateral approach. Once the needle was in place, a linear ultrasound transducer was placed over the eyelid and the spread of local anesthetics was assessed during the injection (real time). Akinesia was assessed by a blinded observer 10 minutes after block placement. The incidence of intraconal spread and its correlation with a complete akinesia was measured. RESULTS: The overall block failure rate was 28% in terms of akinesia, and the rate of rescue blocks was 20%. Clear intraconal spread during injection of the local anesthetic mixture could be detected with ultrasound imaging in 61 of 100 patients. The positive predictive value for successful block when intraconal spread was detected was 98% (95% confidence interval, 91%-100%). The association between clear and no evidence of intraconal spread and effective block was statistically significant (χ test, P < 0.001). CONCLUSIONS: Ultrasound imaging provides information of local anesthetic spread within the retro-orbital space, which might assist in the prediction of block success.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".