Ultrasound‐guided fascial plane blocks of the chest wall: a state‐of‐the‐art review
Bibliographic record
Abstract
Ultrasound-guided fascial plane blocks of the chest wall are increasingly popular alternatives to established techniques such as thoracic epidural or paravertebral blockade, as they are simple to perform and have an appealing safety profile. Many different techniques have been described, which can be broadly categorised into anteromedial, anterolateral and posterior chest wall blocks. Understanding the relevant clinical anatomy is critical not only for block performance, but also to match block techniques appropriately with surgical procedures. The sensory innervation of tissues deep to the skin (e.g. muscles, ligaments and bone) can be overlooked, but is often a significant source of pain. The primary mechanism of action for these blocks is a conduction blockade of sensory afferents travelling in the targeted fascial planes, as well as of peripheral nociceptors in the surrounding tissues. A systemic action of absorbed local anaesthetic is plausible but unlikely to be a major contributor. The current evidence for their clinical applications indicates that certain chest wall techniques provide significant benefit in breast and thoracic surgery, similar to that provided by thoracic paravertebral blockade. Their role in trauma and cardiac surgery is evolving and holds great potential. Further avenues of research into these versatile techniques include: optimal local anaesthetic dosing strategies; high-quality randomised controlled trials focusing on patient-centred outcomes beyond acute pain; and comparative studies to determine which of the myriad blocks currently on offer should be core competencies in anaesthetic practice.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".