The Utility of Erector Spinae Plane Blocks in Breast Surgery: A Practical Review
Bibliographic record
Abstract
Background: In plastic surgery, breast surgery patients are among the most susceptible to postoperative pain. Amidst the opioid epidemic, healthcare goals seek to optimize nonopioid multimodal pain control by including regional analgesia. The erector spinae plane block (ESPB) is among several regional blocks used in breast surgery. Although the paravertebral block has previously served as the gold standard, new research focused on ESPB may shift standards. Methods: A comprehensive PubMed review was performed in September 2023 to identify articles related to ESPB use in breast surgery. Non-English and unavailable articles were excluded. Data extracted included publication year, techniques, and outcomes. Results: Sixty-eight publications were included, of which 31 were randomized control trials (45.6%). Most were published between 2021 and 2023 (n = 40, 58.8%). Most articles that evaluated pain and opioid use suggested that ESPB performed better than nonblocked groups (n = 26, 38.2% of total articles and n = 4, 5.88% of total articles) and performed similarly to other blocks. However, articles that evaluated the pectoral nerve block suggested it outperformed ESPB in these aspects (n = 6 articles, 8.82%). ESPB was shown to be a safe and procedurally short block to perform, effective in the hands of novice providers. Conclusions: ESPB offers reliable outcomes, improving pain control and decreasing opioid consumption. In turn, this can decrease healthcare costs and patient morbidity.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".