Diaphragm-sparing brachial plexus blocks: a focused review of current evidence and their role during the COVID-19 pandemic
Bibliographic record
Abstract
PURPOSE OF REVIEW: Given that COVID-19 can severely impair lung function, regional anesthesia techniques avoiding phrenic nerve paralysis are relevant in the anesthetic management of suspected/confirmed COVID-19 patients requiring shoulder and clavicle surgical procedures. The objective of this review is to provide an overview of recently published studies examining ultrasound-guided diaphragm-sparing regional anesthesia techniques for the brachial plexus (BP) to favor their preferent use in patients at risk of respiratory function compromise. RECENT FINDINGS: In the last 18 months, study findings on various diaphragm-sparing regional anesthesia techniques have demonstrated comparable block analgesic effectivity with a variable extent of phrenic nerve paralysis. The impact of hemi-diaphragmatic function impairment on clinical outcomes is yet to be established. SUMMARY: Existing diaphragm-sparing brachial plexus regional anesthesia techniques used for shoulder and clavicle surgery may help minimize pulmonary complications by preserving lung function, especially in patients prone to respiratory compromise. Used as an anesthetic technique, they can reduce the risk of exposure of healthcare teams to aerosol-generating medical procedures (AGMPs), albeit posing an increased risk for hemi-diaphragmatic paralysis. Reducing the incidence of phrenic nerve involvement and obtaining opioid-sparing analgesia without jeopardizing efficacy should be prioritized goals of regional anesthesia practice during the COVID-19 pandemic.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".