Impact of Regional Anesthesia on Recurrence, Metastasis, and Immune Response in Breast Cancer Surgery
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The perioperative period is critical in the long-term prognosis of breast cancer patients. The use of regional anesthesia, such as paravertebral block (PVB), could be associated with improvements in long-term survival after breast cancer surgery by modulating the inflammatory and immune response associated with the surgical trauma, reducing opioid and general anesthetic consumption, and promoting cancer cells death by a direct effect of local anesthetics. METHODS: A systematic literature search was conducted for studies of patients who received PVB for breast cancer surgery. The Jadad score and Ottawa-Newcastle scale were used to assess the methodological quality of randomized controlled trial and observational retrospective studies, respectively. Only high-quality studies were considered for meta-analysis. The selected studies were divided into 3 groups to determine the impact of PVB on (a) recurrence and survival, (b) humoral response, and (c) cellular immune response. RESULTS: We identified 467 relevant studies; 121 of them underwent title and abstract review, 107 were excluded, and 15 studies were selected for full text reading and quality assessment. A meta-analysis was not conducted because of low-quality studies and lack of uniform definition among primary outcomes. Thus, a systematic review of the current evidence was performed. CONCLUSIONS: Our study indicates that there are no data to support or refute the use of PVB for reduction of cancer recurrence or improvement in cancer-related survival. However, PVB use is associated with lower levels of inflammation and a better immune response in comparison with general anesthesia and opioid-based analgesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| 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 | 0.001 | 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".