Acupuncture against chronic postsurgical pain in non-small cell lung cancer patients
Bibliographic record
Abstract
INTRODUCTION: Video-assisted thoracoscopic lobectomy is the prior recommended treatment for non-small cell lung cancer (NSCLC), with the advantages of small trauma, less postoperative pain, and quick recovery. However, a large number of patients may suffer chronic postsurgical pain (CPSP), which makes the patients unwilling to practice pulmonary exercises, and it would directly affect patient's cough, sputum expectoration, and mobility. Opioids could greatly improve the quality of postoperative analgesia and the quality of life after surgery, but it is accompanied with obvious side effects. A number of clinical studies have proved that acupuncture could improve postoperative pain and reduce opioid use. In this study, we try to conduct a randomized controlled study to evaluate the efficacy and safety of plum-blossom needle acupuncture combined with Tramadol in improving CPSP after lobectomy in NSCLC patients. METHODS: Patients will be randomly divided into treatment group (acupuncture plus Tramadol) and control group (sham acupuncture plus Tramadol) with a random number table in 1:1 ratio. The patients, outcome assessor, and statistician will be blinded. The outcomes are changes of numerical rating scale, Karnofsky performance score, brief pain inventory, blood routine, liver and kidney function. The data will be analyzed by SPSS 22.0. CONCLUSIONS: The results will help to evaluate the efficacy and safety of plum-blossom needle acupuncture in improving CPSP after lobectomy in NSCLC patients.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".