Impact of Peripheral Nerve Block Technique on Incidence of Phrenic Nerve Palsy in Shoulder Surgery
Bibliographic record
Abstract
Peripheral nerve blocks are an increasingly common method of providing postoperative analgesia for shoulder surgeries. However, the standard technique, the interscalene block (ISB), inevitably causes hemidiaphragmatic paresis (HDP), secondary to phrenic nerve palsy. This can cause morbidity in patients with preexisting respiratory compromise, prompting investigation into alternative "phrenic-sparing" nerve blocks. The aim of this review was to give an overview of these blocks and critically evaluate the current literature to determine if any are suitable replacements for ISB. The incidence of HDP and analgesic efficacy were considered. We queried four electronic databases and one register. Twenty-eight original articles were selected for review. The use of ultrasound guidance, lower volumes of local anaesthetic (LA), and injection 4 mm outside the brachial plexus fascia reduced HDP incidence for the ISB; however, no single modification did so sufficiently. While the anterior suprascapular nerve block (SSNB) showed comparable analgesic effects to the ISB, HDP prevalence was also high. The posterior SSNB produced consistently low HDP incidences but also inferior analgesia to ISB, except when combined with an infraclavicular brachial plexus block. The superior trunk block (STB) provided equivalent analgesia to the ISB while reducing HDP incidence, but not significantly. Lower LA volumes consistently led to lower HDP incidence across all blocks, likely due to a reduced ability to spread to the phrenic nerve. Further investigation into the minimum effective volumes of the extrafascial ISB, anterior SSNB, STB, and combined posterior SSNB with infraclavicular block is warranted to determine if any of these blocks can successfully balance HDP prevention with analgesic efficacy.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".