Comparison of the retrobulbar and Peterson nerve block techniques via magnetic resonance imaging in bovine cadavers
Bibliographic record
Abstract
OBJECTIVE: To use magnetic resonance (MR) imaging to describe and compare the anatomic distribution of a lipid contrast medium injected via the retrobulbar and Peterson nerve block techniques in heads of bovine cadavers. DESIGN: Original study. SAMPLE: 5 grossly normal heads obtained from cattle at necropsy. PROCEDURE: Standardized techniques for the modified retrobulbar and Peterson nerve blocks were established. Each cadaver had 1 treatment performed on a randomly selected side of the head; the second treatment was performed on the alternate side of the head. Injections were performed with canola oil, which is an MR-positive contrast medium. Images of heads in the transverse and dorsal planes were obtained with a 3.0 Tesla short-bore MR system. RESULTS: The retrobulbar technique was characterized by widespread distribution of the contrast medium around the periorbital structures; further distribution of the medium was detected along the optic nerve and in the ethmoid turbinates and nasopharynx. After the Peterson nerve block technique, contrast medium was repeatedly located in the pterygopalatine fossa, but distribution to surrounding structures was minimal. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicate that the retrobulbar injection technique results in a greater distribution of contrast medium to the target nerves and surrounding structures, compared with that achieved via the Peterson nerve block technique. This may explain the previously reported clinical impression that the retrobulbar block is more reliable than the Peterson nerve block but is associated with a greater risk of complications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".