Effect of transcutaneous electro-stimulation in postoperative rehabilitation pain treatment in thoracic surgery: a randomized clinical trial
Bibliographic record
Abstract
BACKGROUND: Chest pain is one of the most difficult problems to solve after thoracic surgery. Its correct control is often quite difficult, which can cause complications due to an ineffective cough and superficial respiratory movements. METHODS: This study has been designed with the purpose of studying the value of transcutaneous electrical stimulation (TENS) in the postoperative pain rehabilitation of thoracotomy. A prospective and randomized study has been developed. The patients (n = 109) have been treated after hospital discharge with physiotherapy for 3 weeks. Three groups have been established: experimental (n = 37), control (n = 35), and placebo (n = 37), experimental and placebo including the application of TENS during the physiotherapy protocol. Postoperative pain (McGill test) and spirometry have been studied before and after treatment. RESULTS: The largest between-group discrepancy occurred between the experimental and control groups, 16.77 points (p < 0.001). Spirometry has shown an improvement in FVC (27.11%) and FEV1 (28.68%) (p < 0.001) in the experimental group, which was statistically significant compared to the other groups. CONCLUSION: The use of TENS, as an adjunctive treatment to physiotherapy, leads to an improvement in pain control and spirometry values in patients after thoracic surgery, without producing side effects with the technique. These findings provide physiological evidence for the use of TENS in post-pulmonary surgery and may form the basis for the development of pain managed-based programs in clinics and hospitals. TRIAL REGISTRATION: NCT04964973 (ClinicalTrials.gov). First registration: July 16, 2021. PROTOCOL: https://clinicaltrials.gov/study/NCT04964973 .
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".