Erector Spinae Plane Block Decreases Pain and Opioid Consumption in Breast Surgery: Systematic Review
Bibliographic record
Abstract
Adequate control of acute postoperative pain is crucial in breast surgeries, as it is a significant factor in the development of persistent chronic pain. Inadequate postoperative pain control increases length of hospital stays and risk of severe complications. Erector spinae plane block (ESPB) is a novel regional block that has the ability to sufficiently block unilateral multidermatomal sensation from T1 to L3. By reviewing the literature on ESPB, this paper aimed to elucidate its efficacy in breast surgery analgesia and its role in addressing the opioid crisis in North America. METHODS: PUBMED, EMBASE, and Cochrane databases were systematically searched for relevant articles according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Inclusion criteria included any articles that described ESPB in breast surgery. Exclusion criteria composed of articles that exclusively discussed other kinds of regional blocks. RESULTS: Thirty-two articles including 6 randomized controlled trials were included in this review. ESPB demonstrated superior pain control and less opioid consumption compared with tumescent anesthesia or using no block. However, ESPB showed lower efficacy in pain control compared with pectoral nerve block. Patients experienced less nausea and vomiting and were overall more satisfied with ESPB compared with other pain control modalities. The vast majority of the studies reported the ease of ESPB administration, and only 1 case presented with a complication. CONCLUSIONS: ESPB is a promising form of regional anesthesia that can decrease postoperative pain and opioid consumption when used as part of multimodal pain analgesia for patients undergoing breast surgery.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".