Sympathetic ganglion transcutaneous electrical nerve stimulation after coronary artery bypass graft surgery improves femoral blood flow and exercise tolerance
Bibliographic record
Abstract
We tested the hypothesis that transcutaneous electrical nerve stimulation (TENS) over the stellate ganglion region would reduce sympathetic overstimulation and improve femoral blood flow (FBF) after coronary artery bypass graft surgery. Thirty-eight patients (20 men, 24 New York Heart Association class III-IV) were randomized to 5-day postoperative TENS (n = 20; 4 times/day; 30 min/session) or sham TENS (n = 18) applied to the posterior cervical region (C7-T4). Sympathetic nervous system was stimulated by the cold pressor test, with FBF being measured by ultrasound Doppler. Femoral vascular conductance (FVC) was calculated as FBF/mean arterial pressure (MAP). Six-min walking distance established patients' functional capacity. Before and after the intervention periods, pain scores, opiate requirements, and circulating β-endorphin levels were determined. As expected, preoperative MAP increased and FBF and FVC decreased during the cold pressor test. Sham TENS had no significant effect on these variables (P > 0.05). In contrast, MAP decreased in the TENS group (125 ± 12 vs. 112 ± 10 mmHg). This finding, in association with a consistent increase in FBF (95 ± 5 vs. 145 ± 14 ml/min), led to significant improvements in FVC (P < 0.01). Moreover, 6-min walking distance improved only with TENS (postsurgery-presurgery = 35 ± 12 vs. 6 ± 10 m; P < 0.01). TENS was associated with lesser postoperative pain and opiate requirements but greater circulating β-endorphin levels (P < 0.05). In conclusion, stellate ganglion TENS after coronary artery bypass graft surgery positively impacted on limb blood flow during a sympathetic stimulation maneuver, a beneficial effect associated with improved clinical and functional outcomes.
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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 | 0.000 |
| 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".