Bibliographic record
Abstract
Background & Objectives: Systemic lidocaine and magnesium reduce pain hypersensitivity to surgical stimuli; however, their effects after surgery have not been compared. We aimed to compare the effects of intraoperative lidocaine and magnesium on the postoperative functional recovery and chronic pain after mastectomy. Materials & Methods: In this prospective double-blind clinical trial, 126 patients undergoing mastectomy were randomly assigned to lidocaine (L), magnesium (M), and control (C) groups. Lidocaine and magnesium were administered at 2 mg/kg and 20 mg/kg for 15 minutes immediately after induction, followed by infusions of 2 mg/kg/h and 20 mg/kg/h, respectively. Controls received the same volume of saline. The patients’ characteristics, perioperative parameters, postoperative recovery profiles measured using the Quality of Recovery 40 (QoR-40) survey, pain scales, length of hospital stay and the short-form McGill pain questionnaire (SFMPQ) on postoperative 1 month and 3 months were evaluated. Results: The global QoR-40 scores on post-operative day 1 was significantly higher in the group L than the group C (P = 0.003). Moreoever, in sub-scores of the QoR-40 dimensions, the emotional state and pain scores were significantly higher in the group L than in the group M and group C(P=0.027 and 0.023, respectively). At postoperative three months, SFMPQ and SFMPQ-sensitive scores were significantly lower in the group L than in the group C (P=0.046 and 0.036, respectively).Table 1: The QoR-40 scores among the three groups at preoperative and postoperative day 1Conclusion: The results show that intraoperative systemic lidocaine infusion enhanced postoperative functional recovery and might be a good choice for reduction of chronic post mastectomy pain. Disclosure of Interest: None declared
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.536 | 0.220 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".