Perioperative Intravenous Lidocaine Decreases the Incidence of Persistent Pain After Breast Surgery
Bibliographic record
Abstract
OBJECTIVES: Breast cancer surgery is associated with a high incidence of persistent postsurgical pain (PPSP). The aim of this study was to evaluate the impact of intravenous (IV) lidocaine on acute and PPSP, analgesic requirements, and sensation abnormalities in patients undergoing surgery for breast cancer. METHODS: Thirty-six patients participated in this randomized, double-blinded study. Before induction of general anesthesia, patients received a bolus of intravenous lidocaine 1.5 mg/kg followed by a continuous infusion of lidocaine 1.5 mg/kgh (lidocaine group) or an equal volume of saline (control group). The infusion was stopped 1 hour after the skin closure. Pain scores and analgesic consumption were recorded at 2, 4, 24 hours, and then daily for 1 week postoperatively. Three months later, patients were assessed for PPSP and secondary hyperalgesia. RESULTS: Two (11.8%) patients in the lidocaine group and 9 (47.4%) patients in the control group reported PPSP at 3 months follow-up (P=0.031). McGill Pain Questionnaire revealed greater present pain intensity-visual analog scale in the control group (14.6 ± 22.5 vs. 2.6 ± 7.5; P=0.025). Secondary hyperalgesia (area of hyperalgesia/length of surgical incision) was significantly less in the lidocaine group compared with control group (0.2 ± 0.8 vs. 3.2 ± 4.5 cm; P=0.002). The 2 groups were similar in terms of analgesic consumption during the early postoperative period. DISCUSSION: Intravenous perioperative lidocaine decreases the incidence and severity of PPSP after breast cancer surgery. Prevention of the induction of central hyperalgesia is a potential mechanism.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".