Perioperative Intravenous Lidocaine Decreases the Incidence of Persistent Pain After Breast Surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 it