Diaphragm-sparing nerve blocks for shoulder surgery, revisited
Bibliographic record
Abstract
Although interscalene brachial plexus block (ISB) remains the gold standard for analgesia after shoulder surgery, the inherent risks of ipsilateral phrenic nerve block and hemidiaphragmatic paralysis (HDP) limit its use in patients with preexisting pulmonary compromise. In a previous Daring Discourse (2017), our research team has identified potential diaphragm-sparing alternatives to ISB for patients undergoing shoulder surgery. In recent years, the field has been fertile with research, with the publication of multiple randomized controlled trials investigating supraclavicular blocks, upper trunk blocks, anterior suprascapular nerve blocks, costoclavicular blocks, and combined infraclavicular-suprascapular blocks. To date, the cumulative evidence (pre-2017 and post-2017) suggests that costoclavicular blocks may provide similar postoperative analgesia to ISB coupled with a 0%-incidence of HDP. However, in light of the small number of patients recruited by the single study investigating costoclavicular blocks, further confirmatory trials are required. Moreover, future investigation should also be undertaken to determine if costoclavicular blocks could achieve surgical anesthesia for shoulder surgery. Anterior suprascapular nerve blocks have been demonstrated to provide surgical anesthesia and similar analgesia to ISB. However, their risk of HDP has not been formally quantified. Of the remaining diaphragm-sparing nerve blocks, supraclavicular blocks (with local anesthetic injection posterolateral to the brachial plexus), upper trunk blocks, and combined infraclavicular-anterior suprascapular blocks merit further investigation, as they have been shown to achieve similar analgesia to ISB, coupled with an HDP incidence <10%.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".