Left brachial plexopathy after prone positioning with COVID-19: a case series
Bibliographic record
Abstract
Prone positioning is a strategy shown to reduce mortality in patients who are mechanically ventilated for acute respiratory distress syndrome and has been used in the COVID-19 pandemic. It is not, however, without complications. Barotrauma, pressure sores, ventilator associated pneumonia and peripheral nerve injuries have all been implicated as complications of prone positioning. There have also been several reports of brachial plexopathy in patients who have undergone prolonged mechanical ventilation with prone positioning. Patient characteristics including body weight index, degree of critical illness, and suboptimal prolonged positioning have all been suggested as possible contributing factors, although, there has been less discussion concerning the action of rolling patients, and how it may contribute to the development of injuries. We describe 3 cases of left brachial plexus injury in patients who were consistently rolled on their left sides. Patients presented with isolated left upper extremity weakness without any structural etiology found on imaging. Electrodiagnostic studies subsequently confirmed a left brachial plexopathy in each of the cases. We suggest that the action of proning patients may contribute to injury. This observation has not yet been suggested in the literature, and carries clinical relevance, as greater attention and meticulous care may need to be employed when moving these individuals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| 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".