BRACHIAL PLEXUS NEUROPATHY SECONDARY TO POSTOPERATIVE PRONE POSITIONING FOR MACULAR HOLE SURGERY
Bibliographic record
Abstract
In Brief Purpose: To report a case of unilateral brachial plexus neuropathy after prone positioning for macular hole repair. Methods: Case report. Results: After 7 days of strict prone positioning post-macular hole surgery, a 60-year-old patient developed severe pain and weakness in the left arm. Neurologic examination, imaging, and electromyography showed that the patient developed a unilateral brachial plexus neuropathy. Her strength and pain quickly improved after hospitalization and treatment with intravenous solumedrol, pain control, and physiotherapy. Her best-corrected vision improved from 20/400 to 20/40, however, she was left with frozen shoulder syndrome. Conclusion: After macular hole surgery, prone positioning with persistent abduction of the shoulder such that the patient's hands rest above the head may put patients at risk for a brachial plexus neuropathy. However, this risk may be minimized by the use of positioning assistive devices, reducing the duration in the prone position, instituting frequent breaks, and warning patients to look for signs of neuropathy. After macular hole surgery, prone positioning with persistent abduction of the shoulder such that the patient's hands rest above the head may put patients at risk for a brachial plexus neuropathy. However, this risk may be minimized by the use of positioning assistive devices, reducing the duration in the prone position, instituting frequent breaks, and warning patients to look for signs of neuropathy.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".